Apparatus and method for visualizing periodic motions in mechanical components

ABSTRACT

A non-contacting system for visualizing and analyzing periodic movements in machinery includes at least one video acquisition device that acquires sampling data as a video comprising a plurality of video image frames and a data analysis system including processor and memory, and a computer program operating in the processor to filter movements depicted in the video to a specific frequency to normalize the phase in a plurality of objects in the video representing parts or components of the machinery, to compare the movements of such objects which in some embodiments provides for construction of a modified video to visually exaggerate the apparent movement of the at least one of the plurality of objects.

CROSS REFERENCE TO RELATED U.S. APPLICATIONS

This patent application is a continuation-in-part application of U.S. patent application Ser. No. 16/840,911, with a filing date of Apr. 6, 2020, which claims the benefit of priority to, and is a continuation application of, U.S. patent application Ser. No. 16/106,642, with a filing date of Aug. 21, 2018, now U.S. Pat. No. 10,643,659, issued May 5, 2020, titled “Apparatus and method for visualizing periodic motions in mechanical components”, which claims priority and benefit as a continuation-in-part of U.S. patent application Ser. No. 14/999,660, with a filing date of Jun. 9, 2016, now U.S. Pat. No. 10,062,411, issued on Aug. 28, 2018, titled “Apparatus and method for visualizing periodic motions in mechanical components”, which claims priority and benefit as a continuation-in-part of U.S. patent application Ser. No. 14/757,245 filed on Dec. 9, 2015, now U.S. Pat. No. 10,459,615, issued on Oct. 29, 2019, titled “Apparatus and method for analyzing periodic motions in machinery”, which in turn claims the benefit of each of the following Provisional Patent Application Ser. No. 62/090,729, “Optical detection of periodic movement”, filed on Dec. 11, 2014; Ser. No. 62/139,127, “Method for determining, comparing, measuring, and displaying phase”, filed on Mar. 27, 2015; Ser. No. 62/141,940, “Method and system for analysis of structures and objects from spatio-temporal data”, filed on Apr. 2, 2015; Ser. No. 62/146,744, “Method of analyzing, displaying, organizing, and responding to vital signals”, filed on Apr. 13, 2015; Ser. No. 62/139,110, “Adaptive array comparison”, filed on Apr. 14, 2015; Ser. No. 62/154,011, “Non contact optical baby monitor that senses respiration rate and respiratory waveform”, filed on Apr. 28, 2015; Ser. No. 62/161,228, “Multiple region perimeter tracking and monitoring”, filed on May 13, 2015; and Ser. No. 62/209,979, “Comparative analysis of time-varying and static imagery in a field”, filed on Aug. 26, 2015, the contents of all of which are fully incorporated herein by reference in their entirety and are claimed as a basis for priority.

Likewise, the utility applications claiming priority to the above-referenced provisional applications are incorporated by reference herein in their entirety, being application Ser. No. 14/757,255 filed Dec. 9, 2015 and issued as U.S. Pat. No. 9,704,266 on Jul. 11, 2017, titled “Non-contacting monitor for bridges and civil structures”; application Ser. No. 14/757,256 filed Dec. 9, 2015 and published as U.S. Publication No. 20160210747 on Jul. 28, 2016, and titled “Method of analyzing, displaying, organizing and responding to vital signals”; application Ser. No. 14/757,259 filed Dec. 9, 2015 and published as U.S. Publication No. 20160217588 on Jul. 28, 2016, and titled “Method of adaptive array comparison for the detection and characterization of periodic motion.”

FIELD OF INVENTION

Embodiments herein pertain to apparatuses and methods associated with a non-contacting system for analyzing periodic movements in machinery for predictive maintenance and other purposes by normalizing phase data in video recordings of motion of such machinery.

BACKGROUND

All machines and moving systems produce vibrations of various kinds, some of which may be characteristic of normal operation and others of which may indicate off-normal conditions, unusual wear, incipient failure, or other problems. In the field of predictive maintenance, the detection of vibration signatures is a key element of the diagnostic process in which the goal is to identify and remedy incipient problems before a more serious event such as breakdown, failure, or service interruption occurs.

U.S. Pat. No. 8,515,711 to Mitchell et al. proposes that wireless telemetry systems, including point sensors mounted directly on a turbine component, may provide more accurate measurement of component temperature and vibrations. The disclosed system may comprise a diagnostic system that combines the high fidelity data obtained by the point sensors with the broad area data associated with the same components and obtained simultaneously by surface measurement techniques. Calibration of the surface measurement techniques via point sensors located in the field of view on the same components may result in high fidelity data being obtained from a large surface area of the turbine components.

U.S. Pat. No. 9,006,617 to Mullen describes a monitoring system for monitoring the temperature and vibration of equipment, comprising a central digital computer, a MESH communication network, a plurality of heating elements for heating the equipment, and temperature/vibration sensors adapted to measure the temperature of the equipment. The central computer uses the data it receives from the other elements of the monitoring system to determine when the equipment is not at the correct temperature/vibration and diagnoses the reason why.

U.S. Pat. No. 8,924,163 to Hudson et al. discloses a vibration data collection and rotating machinery fault diagnostic instrument that includes a machine setup engine, a measurement engine, a diagnostic engine, a measurement user interface module, a machine setup user interface module, and a diagnostic user interface module. The means of acquiring vibration data are disclosed to be either single-axis or tri-axial accelerometers.

It has long been known that one can analyze the current in a motor to probe various conditions of the motor and equipment that is driven by the motor, using various signature analysis techniques. U.S. Pat. No. 6,774,601 to Swartz et al. discloses a system and method for predicting mechanical failures in machinery driven by induction motors by using the motor as a diagnostic tool to detect present mechanical disturbances. The motor is monitored during operation to avoid down-time. The motor's torque fluctuations are used as an indicator of early-stage mechanical failures in the machinery. The motor's torque fluctuations are determined using indirect sensing techniques that are less expensive and less intrusive than previously known. More specifically, torque is derived from easily and inexpensively measurable parameters, such as motor slip and phase angle. Current operation is compared to known normal operation. Variations of the motor's characteristics from the known baseline indicate an actual or approaching mechanical failure. “Experimental Fractals” are disclosed that visually represent a current state of the monitored machinery and allow for visual comparison to a baseline for detection of mechanical failures. Future failures are forecast by extrapolating a derived trend. U.S. Pat. No. 6,727,725 to Devaney et al. discloses a method to detect motor bearing damage using wavelet analysis of the motor current transient during startup.

Each of the foregoing methods relies on having physical contact with the machine system, either in the form of mechanical accelerometers affixed to the machinery, or by an electrical diagnostic connection in the power line. Each method also requires a specific physical solution for a specific component

Many industrial processes involve moving components, parts, structures, or workpieces (i.e., objects) that may vibrate in characteristic (and possibly diagnostic) patterns but are not amenable to physical contact. Examples include: a moving paper web, either in raw papermaking or in printing operations, moving sheet metal in rolling, heat treating, and finishing operations; moving items on a conveyor; rolling components such as individual rollers that support an elevated conveyor, moving extruded products, such as metal wire, polymer tubes, and the like. It will be appreciated that in each of these situations, important process control and/or predictive maintenance information may be found. One premise of the current embodiments is to acquire video recordings such as, but not limited to, from one or more video acquisition devices, such as but not limited to one or more video cameras (sensitive to visible, IR, UV, or multi-spectral wavelengths), webcams, or digital cameras integral in cells phones, positioned at a selected distance from an object. Such video acquisition offers advantages as a non-contact sensor and provides information from millions of pixels simultaneously. In the visible light range changes of the light intensity can be related to the motion of objects in the field of view. In the case of light intensity changes, a fundamental unit of measurement is displacement. In some applications, the available accuracy is within a tenth of a mil or less. In addition to the visible light range, other wavelengths in the electromagnetic spectrum may also be used to detect motion or other phenomena of diagnostic importance. The application of mathematical techniques to magnify the motion and the ability to modify the frame rate on replay of the recorded video allows technicians to visually present the motion of concern and provides powerful arguments about what is happening. Often, such information augments the existing need for repair or preventive maintenance to decision-makers, who may have limited technical understanding of the underlying physics of the fault condition. The use of video recordings to capture vibration characteristics of objects in the scene of view is extremely powerful because in addition to the quantitative measurement of a time waveform or frequency spectrum of motion in the scene, a user can visualize the motion of all the objects in a scene. This visualization of the motion can be mathematically modified by amplifying or filtering the video and replaying the frequency(s) of interest at a speed that is optimized for observation by the human eye. This allows a user to present the specific motion of concern in a visually enhanced format that is easily understood by less technically skilled individuals or individuals less familiar with a particular machine or objects.

One of the strengths of video measurements is the ability to determine how one object, or components of an object, is or are moving with respect to another. The timing of a sequence of events in the video or the phase relationship between objects at a specific frequency of interest may be critical to the analysis of the recorded events. When analyzing the movement of components in a machine train, for example, it is often very important to know whether they are moving in-phase or out-of-phase with respect to each other. However, the phase associated with a pixel may be influenced by the edge or direction of contrast at that location. For example, an edge of a first object that goes from white to black and an edge of a second object that goes from black to white may be in phase with each other when it comes to the directionality of motion those edges or areas of contrast are undergoing. However, if one measures the modulation of light (i.e., changes in intensity of reflected light) at those pixels associated with the edges of the first object and the second object, the phase may be found to be 180 degrees apart. Even though they are moving together, one pixel will dim while the other brightens. This issue is compounded if the user attempts to create a phase map for all or a subset of the pixels in the entire field of view.

For all of the foregoing reasons, including the points concerning phase study, what is needed is a general, non-contacting method for analyzing general vibration processes, particularly in a factory or production environment, and visualizing phase relationships between objects in a consistent manner unaffected by the individual elements in the field of view are light or dark. This will facilitate rapid and accurate interpretation of phase relationships within motion captured by the recording.

SUMMARY

Objectives and advantages of the present invention include the following: providing a visualization method for vibrating components; providing a visualization tool that allows motions to be amplified and displayed at a rate that can be seen by the human eye; providing a non-contacting diagnostic tool that uses vibrations to characterize the state of stress in structural components; and, providing a graphical user interface to analyze and manage motion-amplified video images for determining periodic motions in components without the need for edge visualization or other specific feature analysis. These and other objects and advantages of the embodiments herein will become apparent from consideration of the following specification, read in conjunction with the drawings.

According to one aspect, an apparatus for visualizing physical vibrations in mechanical components comprises:

a device for acquiring video image files;

a data analysis system including processor and memory;

a computer program operating in the processor to;

identify an area in the images where periodic motions associated with a mechanical vibration may be detected and quantified, and,

compute a new video file in which the motions are visually amplified; and,

a user interface that displays a motion-amplified video image of the mechanical component.

According to another aspect of the embodiments herein, a method for visualizing physical vibrations in mechanical components comprises the steps of:

acquiring a video image file of a selected mechanical component;

providing a data analysis system including processor and memory to;

identify an area in the images where periodic motions associated with a mechanical vibration may be detected and quantified, and compute a new video file in which the motions are visually amplified; and,

displaying a motion-amplified video image of the mechanical component on a graphical user interface (GUI).

According to another aspect of the embodiments herein, a method for visualizing physical vibrations in structural cables comprises the steps of:

acquiring a video image file of a structure containing a plurality of structural cables within the image field;

providing a data analysis system including processor and memory to;

identify areas in the images where periodic motions associated with resonant mechanical vibrations in the cables may be detected and quantified, and,

compute a new video file in which said motions are visually amplified; and,

displaying a motion-amplified video image of the structure on a graphical user interface (GUI) in which a user can select each cable individually and at least one vibrational characteristic of the selected cable will be displayed.

According to another aspect of the embodiments herein, a method for visualizing physical vibrations in mechanical components comprises the steps of:

acquiring a video image file of a selected mechanical component;

providing a data analysis system including processor and memory to:

identify an area in the images where periodic motions associated with a mechanical vibration may be detected and quantified, and,

compute a new video file in which the motions are visually amplified;

displaying a motion-amplified video image of the mechanical component on a graphical user interface (GUI); and,

exporting the motion-amplified video along with any user-selected metadata.

According to another aspect of the embodiments herein, to correct for the direction of contrast at any edge location, the acquired video is processed with an edge detection algorithm to normalize the motion of edges to remove the effects of white-to-black versus black-to-white. Using this approach, one or more edges associated with the boundaries of an object will move in the same direction and can be coded consistently when creating a phase map of the objects in the field of view. When this phase map is superimposed over a single frame (or, image frame) of the video or a modified video which has been filtered or amplified, it greatly facilitates the understanding of the relationship between the motion of the objects in a field of view.

BRIEF DESCRIPTION OF DRAWINGS

The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee.

The drawings accompanying and forming part of this specification are included to depict certain aspects of the embodiments herein. A clearer conception of these embodiments, and of the components and operation of systems provided with the invention, will become more readily apparent by referring to the exemplary, and therefore non-limiting embodiments illustrated in the drawing figures, wherein like numerals (if they occur in more than one view) designate the same elements. The features in the drawings are not necessarily drawn to scale.

FIG. 1 illustrates schematically the arrangement of video data into a three-dimensional array where x, y are spatial coordinates in a video image and z is time.

FIG. 2 illustrates the result when the frame spacing is non-optimal, in this case, every 8th frame (N=8).

FIG. 3 illustrates the result when the spacing is more nearly optimal, in this case, every 6^(th) frame (N=6).

FIG. 4 illustrates a summed frame of differenced frames from 20 seconds of video data. Note the method isolated motions associated with the breathing based on the selected frame number separation as indicated by the darkest pixels in the summed frame.

FIG. 5 illustrates a simplified flow chart of the basic camera operation and analysis done in a completely automated process.

FIG. 6 illustrates a video frame in which a small paper square having a “+” shaped fiducial mark has been attached to a speaker, which is vibrating periodically in the direction indicated by the white arrow.

FIG. 7 illustrates a difference image, in which the bright lines on the edges of the small paper square indicate motion.

FIG. 8 illustrates another frame in the same sequence as FIG. 7, but in this instance the motion is in the opposite direction, so the bright lines are orthogonal to those in FIG. 7.

FIG. 9 illustrates the result when an amplification factor is applied to the difference image of FIG. 7, which is then overlaid onto the original image.

FIG. 10 illustrates the result when an amplification factor is applied to the difference image of FIG. 8, which is then overlaid onto the original image.

FIG. 11 illustrates a difference frame calculated using the mean frame rather than the median frame as the reference frame.

FIG. 12 illustrates a difference frame calculated using a single frame from the original video file as the reference frame (i.e., the reference frame was neither a mean frame nor a median frame).

FIG. 13A (a-b) illustrates a regular or healthy respiration waveform, and FIG. 13B (a-b) illustrates an irregular and transient event in an otherwise periodic motion, as detected using the present invention.

FIGS. 14A-D illustrate the steps in an exemplary analysis for the specific case of respiration. 14A shows the result using a four-frame separation; 14B shows eight-frame separation; 14C shows nine-frame separation; and 14D shows nine-frame separation but starting with a different initial frame.

FIG. 15A-B illustrates the ability of a user to rewind the acquired data files (indicated conceptually by the large arrow) to return to a point in time at which an event occurred.

FIG. 16 illustrates an example of a user interface implemented for mobile devices according to another aspect of the embodiments herein.

FIG. 17 illustrates the steps in one approach for determining and displaying phase according to one aspect of the embodiments herein.

FIGS. 18A-C illustrate the use of phase information to analyze vibrations in a bridge. FIG. 18A shows a video frame of a bridge after a truck (not shown) has passed over it. FIG. 18B shows a single phase mask at 2.25 Hz, which is the fundamental frequency of the bridge. FIG. 18C shows the same phase mask but multiplied by the intensity of the movement at each pixel.

FIGS. 19A-B illustrate the use of motion amplification to visually exaggerate the apparent movement of an object. FIG. 19A shows a video frame of a motor that is driving a shaft and flywheel (not shown). FIG. 19B shows another frame in the amplified video clip, representing the point of maximum displacement relative to the frame in FIG. 19A.

FIGS. 20A-C illustrate the conceptual basis for motion amplification. FIG. 20A shows idealized and non-ideal edges moving to the right. FIG. 20B shows the non-ideal edge moving to the right with amplification factors indicated. FIG. 20C shows the edge moving to the left with amplification factors indicated.

FIG. 21 illustrates schematically a graphical user interface (GUI) in accordance with one aspect of the embodiments herein.

FIGS. 22A-C illustrate three (of many) possible sequences that may be selected by a user via the GUI. In FIG. 22A, the processor selects a reference frame, applies a default amplification factor, and the user exports the processed video. In FIG. 22B, the user selects a desired amplification factor, after which the processed video is exported. In FIG. 22C, the user selects a desired amplification factor, and the amplified video is displayed but not immediately exported.

FIG. 23 illustrates a problem associated with making phase measurements when objects move across light-to-dark or dark-to-light boundaries, as identified by the edges marked with dotted rectangles for vertical motion or the vertical edges marked with dotted circles in the presence of horizontal motion.

FIG. 24 illustrates the out-of-phase relationship which occurs if two accelerometers are mounted on opposing surfaces of an object.

FIG. 25A-B provides a flowchart depicting an algorithm employed in an embodiment to normalize the phase from a video recording.

FIG. 26A shows the original image of an object with a 10 Hz vertical sinusoidal motion.

FIG. 26B shows an unnormalized image of phase of an object with a 10 Hz vertical sinusoidal motion.

FIG. 26C shows a normalized image of phase of an object with a 10 Hz vertical sinusoidal motion that shows that all the boundary lines appear correctly to be moving in phase.

FIG. 27A shows the original image of a machine train which is experiencing vibratory motion.

FIG. 27B shows an unnormalized phase map superimposed on the video frame showing distinct edges on the vibrating machine train with incorrect phase relationships.

FIG. 27C shows a normalized phase map superimposed on this video frame showing distinct edges on the vibrating machine train correctly moving in phase.

FIG. 28A shows the original image of a bridge with a handrail which is experiencing vibratory motion.

FIG. 28B shows an unnormalized phase map superimposed on the video frame showing the motion on an edge of the bridge handrail out of phase with the remainder of the handrail.

FIG. 28C shows a normalized phase map superimposed on the video frame showing the edge of the vibrating bridge handrail correctly moving in phase with the rest of the handrail.

FIG. 29 is a screen shot from a user interface indicating some aspects of a phase overlay operation including user selections to be performed by a system or method in accordance with the present embodiments.

FIG. 30A illustrates an operation directed to a phase study accentuating vertical edges of components in moving objects shown in the image to emphasize horizontal motion.

FIG. 30B illustrates an operation directed to a phase study accentuating horizontal edges of components in the moving objects to emphasize vertical motion.

MULTIPLE EMBODIMENTS AND ALTERNATIVES

Present embodiments are directed to non-contacting systems to observe moving components using a video camera, to find areas of maximum movement, to characterize the periodic behavior of those movements (e.g., amplitude, phase, and waveform), and in some aspects to normalize the phase of movements of a plurality of objects in relation to one another. The data may be time-stamped and archived for later comparison to other similar equipment or to other data taken from the same component at a different time or under different conditions, to provide knowledge to a predictive maintenance system. The system may include a graphical user interface (GUI), which may display various parameters relating to the measured data, a frame or frames from the video input file, phase masks and motion amplification, time stamps or other identifying information, and user-entered information describing relevant conditions (speed, power level, load, time of day, etc.). It may further allow the user to draw a perimeter or region of interest within the video frame so that analysis may be focused on that region and extraneous movements elsewhere in the frame may be ignored. The system may include an archived database, with which the user can compare historical data in order to determine trends and warnings of machine deterioration, wear of bearings and couplings, loose components, or other symptoms of the need for maintenance before a failure, shutdown, or other catastrophic event can occur.

The invention may use several novel techniques to:

1. Analyze the video file by an adaptive array comparison technique to find a selected number of pixels that have the most variation over time, i.e., the most physical movement. 2. Find the best frames to use (i.e., optimal frame spacing) to maximize the frame differences and best determine the periodicity of the vibration. 3. Apply various mathematical functions, such as fast Fourier transform analysis (FFT) to derive richer physical information from the observed vibration waveform.

As used herein, the terms “machine”, “machinery”, and “mechanical component” are intended to be taken in their broadest sense, to include any mechanical component that may exhibit some periodic movements. It includes, for example, motors of all kinds (electrical, internal combustion, turbines), components and linkages connected to or driven by motors; machine tools, grinding wheels and tool bits; electrical and hydraulic actuators; pumps, blowers, fans, pipes, ducts, and other fluid- or air-handling equipment; aircraft components (wings, airfoils, control surfaces of all kinds, landing gear, struts); trackways, conveyors and materials or components conveyed thereon; and any parts, products, and workpieces that are moving through a production environment. Examples of the latter include paper moving through a papermaking machine or reduction line; paper or plastic sheet moving through a printing process; sheet metal being rolled, coated, treated, galvanized, hot-dipped, or otherwise handled in a continuous or semi-continuous form; wire or polymer fibers being drawn or extruded; and woven and nonwoven fabric being produced, dyed, printed on, or cut. It may also include components that are not directly driven or intended to move, but which are subject to incidental vibrational inputs, such as components in a motor vehicle that are incidentally vibrated by the engine or by movement on a road.

As used herein, the term “vibration” refers to any physical movement that may be characterized by some periodic change of position as a function of time. Vibrations may be periodic, such as, e.g., sinusoidal, symmetric sawtooth, asymmetric sawtooth, or they may be aperiodic or noisy. Vibrations may have any waveform, which may include waveforms characteristic of superimposed vibrations of different frequencies, amplitudes, and phases.

In the examples that follow, various aspects of the embodiments herein will be made clearer, and applications to various monitoring problems will be illustrated. These examples are not intended to restrict the scope of the embodiments herein to the particular implementations described.

Method of Adaptive Array Comparison Example

One way applicants have developed to analyze a video stream to extract waveform information is Adaptive Array Comparison, which may be described generally as follows: A video signal includes multiple frames separated in time and each frame includes multiple pixels. Each pixel value in a particular frame is compared to the values of corresponding pixels in different frames of the video signal to search for periodic signals occurring at the pixel locations over time. Periodic signals are defined in time with a maximum and minimum peaks. These two locations give the maximized absolute value in the difference in two locations of the periodic signals. This fact can be exploited to filter and locate pixel locations where the periodic signal gives the largest signal to background or signal-to-noise in a scene or image sequence. In the case of human respiration, we know the adult rate typically lies in the approximate range of 12-20 breaths per minute. This corresponds to 0.25 to 0.33 Hz. A medium rate would be 16 breaths per minute or 0.27 Hz. For a 15 fps camera that corresponds to a breath every 56.25 frames. This would tell us that a video of a person breathing may include periodic signals at certain pixels having a maximum and minimum at a difference of approximately 28 frames apart. This is the initial frame difference we use to locate the best pixels to track. We difference several series of images at this separation, meaning the value at each pixel location in one frame is subtracted from the value at the corresponding pixel location at a second frame. Then, the second frame is differenced with a third frame and so on. For each pixel location, the absolute values of their differences are added together. Then some number of pixels with the highest sum of differences are selected to be tracked. There is the potential that the chosen frames happen to be 90 degrees (or some other phase shift) out of phase with a maximum and minimum; so, in the event that no initial peaks are found, we recalculate with a 90-degree phase shift (or some other phase shift). Once we find the correct pixels to track, we then begin peak counting for each selected pixel, noting the phase of the waveform. Once we have sufficiently determined the precise phase and frequency of the waveform, we recalibrate, making sure to frame difference such that the number of frames between differences exactly equals the difference between a maximum and minimum of the waveform, as well as starting in phase so the difference is aligned with the maximum and minimum. The processor runs the code to do this like any other programs, although a specialized piece of hardware specifically designed to do this is not necessarily used (in contrast to the way decoding HD video is typically done).

Applicant has found that the foregoing method adapts to a particular component's waveform even if it changes. In the example of machinery, different frequency rates can be chosen to start with, based on some rudimentary knowledge of the equipment or previously stored user data. For example, passive electrical equipment might be expected have a vibrational displacement corresponding to 60 Hz or some harmonic thereof; a motor or linkage might have most of its vibration at a frequency associated with its rotational speed. The inventive method inherently filters unwanted frequencies. Because of the selected time difference between frames, we reject signals associated with frequencies other than those related to the process we are monitoring.

Example

An image sequence is obtained through a video camera and stored into memory. A video sequence as shown in FIG. 1 has dimensions of space in the x and y axis while having the dimension of time in the z axis and can be thought of as a 3-D data space. The video sequence contains a periodic or recurring motion of interest that is to be extracted from the data set. For example, a feature of interest may be occurring at a given pixel and we may be interested in monitoring that feature. We can use its temporal behavior to find and locate that pixel without any knowledge of the scene, the surrounding pixels, the spatial characteristic of the local pixels or where that pixel may be.

Method of Adaptive Array Comparison for the Detection and Characterization of Periodic Motion Example Mathematical Description

An image frame is defined as an X-Y matrix. A video file is a set of image frames defining an X-Y-t matrix.

For each pixel (i,j) one can calculate the difference (D_(i,j))^(M,N) between the value at pixel (i,j) in one frame and the value in another frame, where M is the starting frame number and N is the spacing between the two frames. So (D_(2,3))^(4,9) would be the difference in value or intensity at pixel (2,3) in frame 4 compared to that in frame 13.

The difference matrix is then summed (preferably in quadrature) to yield the total difference in pixel intensities between frames M and M+N. Difference matrices are calculated for various values of M and N, to find M and N that give the highest value of summed differences. Then a selected number of pixels (i,j) having the greatest difference are chosen and their intensities are tracked over time to determine the periodicity of movement.

There is generally a limit on how long one does this. For example, at 15 fps for 20 sec there are 300 frames, so if N is 10, we difference 29 times (accounting for the ends) or less as M is increased.

Once this is done initially and we have found the location of a peak and the peak separation we redo the calibration with a specific M and N to get it exactly on the peak. M would now be the frame number where an expected maximum or minimum occurs, and N would be the value of ½ of a waveform. This introduces the novel aspect that the process becomes essentially adaptive.

Example

The method and functions of Adaptive Array Comparison may be described as follows:

1. Video Sequence comes in at some frames per second (fps) 2. Some seconds of that data is continually buffered. (Previous frames are overwritten with new frames.) These are the frames we process. 3. From the buffered frames [1], [2], . . . [n] we select a multiple of frame differences N to calculate, e.g., every 4^(th) frame (N=4) or every 5^(th) frame (N=5), etc. This allows us to find the best periodic motion rate. We select the time range between frames based on the range of periodic motion we are interested in finding. This also acts as a band pass filter giving preference to the periodic motion rate within this range. 4. We also offset these frames, say, starting with the 1^(st) frame, then the second, etc. This allows us to find the best phase. So, for example if we are subtracting every 4^(th) frame, we first would do the 1^(st) frame minus the 5^(th), the 5^(th) minus the 9^(th), and so on. Then we would do the 2^(nd) frame minus the 6^(th) frame, then the 6^(th) frame minus the 10^(th). 5. For each test, frame differencing is conducted for some number of frames. For example, 11 frames may be used, and 10 frame differences will be calculated. Each frame difference will be an array of absolute value numbers with each position in the array corresponding to a pixel location. 6. After 10 frame differences are calculated the square of the frame difference arrays are added together to produce a total frame difference array. Then the total frame difference array is analyzed to find the array locations having the largest values. For example, the ten largest values in the array may be found and the array locations containing those values are recorded. When we subtract the frames, we add all the differenced frames from the buffered video in quadrature, meaning we square the difference values so that negative and positive differences don't cancel. Motion in opposite direction shows up in the difference frame with an opposite sign but we want that to add positively, otherwise back and forth motion can show as zero in the sum of the differenced frames. 7. From all the total frame difference arrays across all the multiples and offsets we find a selected number of pixels that have the largest values, say, the brightest 10 pixels. These pixels represent the largest periodic motions in the scene that are between the rate boundaries set in Step 3. 8. Those pixels' values are tracked over time and this will plot the motion waveform. The signals from the tracked pixels are not necessarily in phase. Any two signals could be 180 degrees out of phase and still provide the same information about the existence and the frequency of the same time periodic signal. For example, the signals of all pixels could be out of phase with all other signals, but still correspond to breathing of a subject. Thus, the frequency of each signal would be the same. 9. From the motion waveform we determine the rate and phase. In terms of frame differencing this translates to the peaks in the waveform occurring every N^(th) frame and the waveform starts at frame M. 10. From the information in Step 9 the Adaptive Array Comparison method adapts since we know where the peaks start and how many frames apart, they are. 11. Now we subtract frames with a separation exactly equal to the separation between a maximum and minimum in the waveform. We also make sure to start this process exactly on a peak or minimum. This optimizes the rate and phase to precisely select the motion waveform. 12. This process can be repeated based on a number of factors: a. Time—The method can reiterate every n seconds to ensure it is optimized for the existing waveform. b. When there is a large excursion in the measured waveform the process can restart as this can be due to a motion event. c. The process can restart based on a trigger from an external motion detection method. d. The process can be restarted on buffered data (the 20 seconds of previous video) when an alarm is triggered, (for example, no peaks are detected) to ensure the alarm is accurate. For example, if the waveform is accidentally lost this step could check the last 20 seconds of data to see if the waveform can be detected in another pixel or set of pixels.

Example

FIGS. 2 and 3 illustrate how the system selects the frame spacing by trying two different spacings and comparing the magnitude of the peak differences. In FIG. 2, every 8^(th) frame is used; this turns out to be a bad or “null” spacing, as the sum of differenced frames for a given pixel is zero. When the spacing is changed to every 6^(th) frame, the sum of differenced frames for a given pixel is 30, indicating a much higher signal for this waveform that distinguishes it from the rest of the pixels.

So therefore, we would try a multiple of spacings that represent a reasonable range of vibration rates. We would also offset them or change the phase because the best spacing that perfectly finds the peaks and valleys is also a “null set”. Now we know where to look when this gives us the pixel with the largest sum of the differences. From this pixel we track and locate the peaks and valleys of the waveform. Then we adapt to the individual component's waveform with the next calibration.

Example

FIG. 4 illustrates the summed frame of differenced frames from 20 seconds of video data collected on a human subject. Note the method has successfully isolated motions associated with the breathing based on the selected frame number separation: here the darkest pixels identify the location of greatest motion found in the chest region from the up/down right/left motion of breathing as seen by the camera. These are the pixels that would then be tracked to determine the breathing waveform. It is important to emphasize that the data presentation in FIG. 4 is not an image per se, but rather simply a graphical display of the pixels (dark) that show the most movement. It is also important to emphasize that the entire process was conducted by the processor in an essentially autonomous operation.

Example

An explicit example of the calculations can be shown as follows: Let [1] be the first frame of a video sequence, a 640×480 image, so that [1] is a 640×480 array. Likewise [2] would be the second frame making up a new 640×480 array and [3] would be the third. We would like to sum the difference of every x frames. To ensure the difference is positive we subtract the frames, then square the difference, and then take the square root. Finally, we sum all of differenced frames.

For example, if we difference every 8 frames the calculation would be of the form:

{square root over (([1]−[9])²)}+{square root over (([9]−[17])²)}+{square root over (([17]−[25])²)}+{square root over (([25]−[33])²)} . . . =[SUM]

Other potential applications and features of the embodiments herein include the following:

A user defined setting can be selected to narrow the window on which rates to look for, e.g., 60 Hz in the case of equipment running on standard AC power. It will be appreciated that narrowing the window allows the system to converge more quickly on an optimal frame rate, because this reduces the number of iterations the system has to go through, making it quicker and more accurate as the chance of error would be reduced by eliminating certain frequencies.

Information such as a profile of a particular components characteristic vibration rate, may be stored and later retrieved so the device has a better range of expected rates to look for a priori. In this case the user selects a profile that the device has gathered over previous uses or parameters that were previously entered.

Sections of the video scene may be selected to narrow the search. For example, if the video camera is looking at the edge of a moving paper web, the user interface might allow the user to draw a box (e.g., on a touch screen) to select only the sheet of paper, eliminating the rest of the production environment. Eliminating extraneous parts of the image from consideration will allow the calculations and optimization to proceed more quickly.

One can isolate periodic motion by selecting the range the motion is expected to be in. For example, a particular pump might have a standard speed at which it rotates or reciprocates, which will suggest a reasonable range of frequencies to start with.

The data can be used with a standard peak finding method to determine the maximum and minimums of the waveform.

Example

FIG. 5 presents a simplified flow chart of the basic camera operation and analysis done in a completely automated process. The input video may be MPEG1 simple profile or MJPEG from, e.g., a USB webcam or similar source. The initial calibration subroutine, using 20 s of video, locates the 5 pixels with the greatest values in difference matrix, and establishes State 0, or initial calibration. The waveform tracking and processing subroutine tracks pixels, continuously outputs the last N values of the pixels determined to be the greatest from the difference matrix; processing is done on waveform to determine output states. States 1-3 will be determined in this routine based on processing of the waveforms. An ongoing calibration subroutine is continuously looped; this uses 60 s of frames, summing the difference of frames and locating the five pixels with greatest values in difference matrix. Five output states are continually outputted from the subroutines through a physical channel. Off State condition may be determined by a selected trigger, implemented either in hardware or software.

Example Calibration Subroutine

A subroutine recalibrates the location to find the best pixels. This consists of going through the process of frame differencing again and locating the 15 highest valued pixels in the summed array. The duration of the calibration can be programmatically controlled as well as the frame numbers to difference as a result they may vary. For example, we choose to difference every 4 frames of a 16 fps camera for 40 seconds resulting in 160 differenced frames. Note this in different than the initial calibration since it is limited to 20 seconds.

This recalibration process continually goes on in the background while the waveforms are being outputted from the pixels and the peak finding is performed.

It is important to keep in mind that the mathematical techniques of the present invention derive parametric outputs whether or not an image is ever created or portrayed. Thus, techniques of the present invention may be used with monitors that do not display images, do not recognize objects, and do not have frequent human observation or similar participation. For example, the present invention may output a commonly reported characteristic such as a breathing rate or heart pulse rate or phase or a lag interval or a dimension of a periodically moving object or a period of a motion, or a timing or a motion or other information associated with a periodic motion event without displaying an image thereof. Conversely, in some examples, the user interface may include actual video images, which may be selected to correspond to a particular point in time when an output parameter has a particular value or the waveform displays a particular feature (e.g., an episode when unusual vibrations or some instability appeared temporarily).

As used herein, the term “video” describes any data set representing a series of digital (i.e., pixelated) images of a scene, taken at fixed time intervals, so that the data represent points in X-Y-t space. The image may represent a pattern of reflected and/or emitted visible light, UV, IR, X-rays, gamma rays, or other electromagnetic radiation detected by a two-dimensional position-sensitive detector. Although certain standard file types have been described in some of the examples, the invention is not limited to any particular frame rate or file format.

It will be further appreciated that the invention is not limited to any particular type of image acquisition hardware; video cameras, webcams, digital cameras integral in cell phones, etc., may also be used to generate the raw data files. Alternatively, the raw video may be generated elsewhere and downloaded to the processor from a USB memory device, a CD or DVD, or via an ethernet or wireless connection. The digital imaging device may further include any suitable lenses or other optical components, such as telescopes, microscopes, etc., as are well known in the art. In particular, the invention may be used for examining periodic movement in small MEMS devices or micro-actuators, which could be observed using a video microscope for quality control or other purposes. Adapted to a telescope, the invention could be used, e.g., to study vibrations in ships, helicopters, missiles, etc.

Many examples of the present invention are completely general in that they do not require or insist upon a blob that must be identified with an object in the field of view or with a contour segment that must be associated with an object in the field of view.

Techniques of the present invention may be applied to a variety of imaging modalities, including visible imaging, thermal imaging, multispectral imaging, or hyperspectral imaging. In fact, these are entirely different and independent media having different sources and different mechanisms and different physical significances. However, the techniques for measuring motion remain the same for any spectral ranges. For example, the use of visible images of an object of interest overlaid (or interleaved, overlapped, interspersed, approximately synchronized, or truly simultaneous) with near or far infrared images may yield two effectively independent views of an object of interest. If reflected visible light reveals a periodic motion which may be associated with a structural vibration or some other periodic motion, and a thermal image (perhaps due to friction or an electrical problem) reveals a similar periodic motion location proximate to the visible finding and similar in phase, frequency, or amplitude, or all three, then this improves the likelihood of an accurate result rather than a false positive or false negative finding.

As noted above, the imaging systems may have multiple inputs. These may comprise two visible cameras, an infrared imager and a visible camera, a camera and another input other than an imager such as an ultrasonic sensor or a temperature or a pulse monitor or some other combination of two or more imaging devices.

Example

In a system in which material is being processed in the form of a moving web, e.g., papermaking, it may be advantageous to position two video acquisition systems opposite one another so that each is recording images of opposite edges of the web. In this setup, it will generally be preferable to have both systems synchronized with a common time stamp so that coupled vibration phenomena may be detected and quantified.

The rate of video frame acquisition may be adjusted, e.g., to correspond with a naturally repeating feature on the moving web, such as a printed page. In this way, every frame would capture substantially the same view, and periodic vibrations may be more easily discerned. In a case such as the cold rolling of metal sheet products, the frame rate may be adjusted to correspond to the linear equivalent of one revolution of the processing rolls, so each frame represents a part of the sheet that contacted the same area on the roller.

The inventive technique is not limited to a particular wavelength of light. Different colors are represented by different wavelengths of light, e.g., 550 nm is green. Amplitude changes that are detected by this technique can be restricted to a single wavelength of light or represent a summed intensity change over multiple wavelengths. Each wavelength can be measured independently or together (mono grayscale). The inventive technique may, for example, monitor only the green, blue or red wavelength or monitor the sum of all three.

Electromagnetic Wavelength options. In addition, the inventive technique is not just limited to visible wavelength of light, but can be used in the near IR, far IR, or UV. The technique could be extended to any sensor type that can measure changes in light levels over time whether from reflective or emissive sources. Visible light is generally although not always measured as a reflection. Thermal IR light is generally but not always an emission from the surface. The invention works regardless of whether or not the target is reflecting or emitting the light.

Sensor selection. The sensor type can be chosen based on the scene or target. For example, if the scene is completely dark, void of a visible light source, a thermal IR sensor may be used to monitor the changes in light levels. Also, if a particular material or substance is the target and light level changes are due to a property of interest on the target another sensor type may be chosen. For example, with gas that absorbs in certain wavelengths, or more generally chemicals, a particular sensor that detects those properties may be chosen. For example, one may be interested in using the technique to find an exhaust or chemical leak in the scene based on light intensity changes from the leak specifically associated with the absorption and/or emission at certain wavelengths. Another example may be a flowing liquid that absorbs in certain colors, and that flow changes or pulsing may be indicated by intensity changes in a certain wavelength of light, then a sensor particularly sensitive to that wavelength of light might be chosen.

Interpreting measurement information. The inventive technique can also be used to garner information about the type of change. A particular change using a thermal sensor would indicate that the temperature is changing, whereas a change in color may indicate the target is changing is absorption or emission properties. A change in amplitude could also be indicative in a change in position or vibration, whereas a change in position of the signal being detected from pixel to pixel in time may give information about displacement.

Example

Because temperature is an important factor in predictive maintenance, a GUI may display a video frame of a component along with an IR or thermal image, either separately of as an overlay (which may be calibrated color or grayscale) for a more complete interpretation of the entire data suite.

Comparing multiple measurements. Ratio or comparisons of color changes or amplitudes of certain wavelength can also be used. For example, it may be useful to locate a pixel that changes in intensity from blue to red. This could be indicative of certain properties of interest. An example would be characterizing the uniformity of printing or dyeing on a paper or fabric web. Multiple sensors could be used for this technique or a single sensor with wavelength filters applied (such as a typical color camera). Certain features of interest may be indicated by relationships between multiple sensor sensitivities or wavelength of light.

Redundant and independent inputs. Multiple sensor types or wavelength detections could also provide multiple detections of the same phenomenon increasing the confidence of detection. For example, the light intensity changes due to the periodic vibration of a duct may be detected with a visible or IR camera pointed at the duct wall while another sensor looks at the intensity change in thermal IR from temperature changes around an inlet or outlet of the duct. The technique is then used in both cases to strengthen the detection scheme.

False negative findings. Multiple wavelengths could be used to discern or improve findings which may be false positive and false negative findings and true positive and true negative findings. Intensity shifts from multiple wavelengths, red, blue, green, IR, etc. could be used in conjunction with each other to improve the signal to noise ratio and also provide multiple independent readings to increase confidence in detection.

Measurement duration. This technique could be used with signals that are repetitive but only over a short time duration, e.g., vibrations that arise in forging or stamping operations. The technique could be applied to shortened windows of time to catch signals that occur only for a set duration. Furthermore, it could be used for signals that continually change over time but are ongoing. For example, with a signal that speeds or slows, the time that is used to calibrate or search for a certain intensity change could be shortened to be less than the time change of the signal itself.

Transient event. Additionally, there may be irregular or transient events in a periodic signal. This technique could be used in a short enough time window or in a sufficient sequence of waves to extract the location of a periodic signal in the presence of irregular or transient events. FIG. 13B (a-b) shows an irregular and transient event in an otherwise periodic motion. If the sample window for the technique described here is properly placed the maximum and minimum of the periodic signal can be located. Multiple phase offset would help to address this issue by building up a pixel's sum of differences at a time that the phase offset for a starting point has brought it past the irregular or transient signal occurrence.

Spatial proximity. This technique can find multiple pixels of interest. Spatial relationships between the pixels of interest can further be exploited. For example, if a larger number of pixels of interest were selected and the vast majority of them were found to be in close proximity to each other that could indicate those pixels are related to the same physical phenomenon. Conversely, if they are spread apart and there appears to be no spatial coherence or statistical meaning among the spatial relationship or they are randomly spaced that could indicate they are not related. Furthermore, this technique could also be used to increase confidence in the signal or improve findings which may be false positive and false negative findings and true positive and true negative findings. For example, in a motor-driven pump, there are likely to be many pixels of interest found near the coupling. We could expect a certain percentage to be heavily localized. If this is not the case it may lower our confidence that the vibrations of the machine are being detected. Conversely, if a large number are heavily centralized, we may be more confident we have located a physical region undergoing motion from vibrations. The confidence may be set by a weighted spatial location mean of the pixels or average separation distance, or standard deviation from the spatially averaged locations of all pixels of interest.

Cycles per minute. Intensity variations for different pixels of interest can be indicative of certain phenomena of interest. By limiting the temporal separation of which the pixels are differenced, and the differenced sum is obtained we can filter for phenomena of interest. For example, if we are interested in a rotating or reciprocating machine, we would preferably limit our frame separation to maximum and minimum separation time of waveforms that correspond to the rate of rotation or reciprocation.

Re-calibration—finding a pixel of interest. It is possible after the technique adapts to find the suitable or best separation to get the largest intensity change based on the differencing of maximum and minimum frames, a new search can be performed with that knowledge with tighter constraints to search out specifically that waveform. In that sense it is adaptive after it uses more liberal parameters to find the initial signal. It is possible that a user's information or information on a subject or phenomenon may be stored. The technique can now be used with a priori knowledge of rate, phase etc. to speed up finding the pixels of interest. For example, a user may store the profile of a particular machine or class of machines, and the technique is then used with knowledge of that data. That way, fewer cycles need to be performed and a tighter constraint can be placed on the technique to find the pixel of interest.

Visible and infrared photons. Variation in the intensity of pixels may not always result from radiation emitted or reflected by a single object. For example, if something is moving and at a different temperature than the background, that object may move back and forth periodically blocking a small portion of the background. To a thermal sensor, a pixel detecting light in that region will see an increase and decrease in brightness from the object moving back and forth as the object at T₁ and then the background at a different temperature T₂ are alternately imaged by the pixel.

Multiple cameras. Multiple cameras can be used for multiple detection schemes. They potentially could run at different rates. It is possible to temporally align frames so that certain frames occur at the same time. In this scene the resulting detection of a signal can be temporally aligned as well and correlated. Cameras could potentially be set to image the same field of view. Pixels across multiple cameras or sensors could be correlated so spatial relationships of the pixels in the image of each camera is known.

Other sensors. Other inputs could be correlated to one of more cameras. The detected signal could potentially be correlated to another input signal as well. For example, a proximity probe could be used to validate or increase the confidence of a detected signal determined from a pixel of interest from the technique. Tachometers, accelerometers, and tonometers are all examples of types of sensors that could be used in conjunction with the inventive technique. These input signals could also provide frequencies or phase data to allow the system to use tighter constraints to reduce the number of iterations it goes through or immediately determine the proper phase and or frequency from which to select the differenced frames. These inputs also can be used as triggers.

Single Pixel and Combination of Many Pixels

Techniques of the present invention may be used with the smallest achievable pixel size or may be used with binned pixels where multiple neighboring pixels are collectively associated (mathematically or statistically) to create a larger virtual pixel. This technique may be done on camera or chip or done afterwards in data processing. Binning may be done horizontally, vertically, or both, and may be done proportionately in both directions or disproportionately. Collective association or binning may potentially leave out or skip individual pixels or groups of pixels. For example, one form of collective association may comprise combining a plurality of bright pixels while ignoring some of all of the pixels not determined to be “bright” or “strong” considering a characteristic of interest such as a selected frequency range or amplitude.

Gaining confidence by eliminating false findings. It may be of interest to increase the confidence of the detection by exploring neighboring pixels. Even if those pixels were not chosen as the ones exhibiting the largest motion they can be explored to determine if at least one or more exhibit the same or strongly correlated waveforms to the pixel of interest. If a physical phenomenon that one is trying to detect is expected to be larger than one pixel, it stands to reason that neighboring pixels undergo similar behavior. Thus, it will be clear that this could be used to eliminate false positives in a detection scheme.

Multiplexing. The inventive technique can be applied in a single pixel variant in which an optical element would be used in a multiplex mode where the optical element scans the scene and the transducer samples at each point in the scene. An image or array is created from the scanned data. The sampling/scanning rate is contemplated to be high enough to effectively sample the scene at a fast enough rate to see time-varying signals of interest. Once certain pixels of interest are located, the system would then need only scan those pixels until a recalibration is needed.

Searching a plurality of frequencies. One can compare amplitudes of different subtracted frames separation values, or multiple sums of subtracted frames separation values. For example, comparison can be made between the sum of the subtracted frames for separation X and for separation Y. The frame separations are indicative of frequencies. This comparison will allow one to compare amplitudes of signal changes for different frequencies. Multiple frames separation values that give information about amplitudes of a frequency of the signal can be used to construct a frequency spectrum for a single pixel or multiple pixels.

Arrays representing subtracted frames or sums of subtracted frames at certain frame separation values may be indicative of a frequency. Those arrays may be compared to indicate information about the signals. For example, if two arrays are determined that are indicative of frequency f₁ and f₂, we may compare those two arrays for determine the spatial distance between the phenomenon that is causing the frequencies. In this case the array may be a spatial image.

The following example will more fully illustrate the inventive method, applied specifically to the case of monitoring respiration, as described in Applicant's co-pending application.

Example

Initial calibration with a single frame separation value and starting point for frame differencing does not optimize the differenced values specific to the respiration rate or maximum and minimum values in the chest motion. To solve this, we select multiple frame separation values, all at multiple starting points, to ensure that we find the optimized signal of interest. A series of waveforms, FIGS. 14A-D demonstrates this principle.

Here we see that at 4 frames of separation, FIG. 14A, the separation does not align with the maximum and minimum peaks in the waveform. Aligning with the maximum and minimum peaks would give the strongest signal indicating that the right separation or rate has been found.

FIG. 14B shows the effect of changing the separation between differenced frames to every 8 frames. One can see that this is better but not quite optimal. Next consider 9 frames, FIG. 14C. To ensure that all possibilities are considered we want the option to select a range for frame separations to subtract as well as the increments in spacing. For example, we subtract from every 2 to 30 frames in increments of 4, or generally we subtract from N₁ to N_(n) in frame separations in increments of ΔN.

In addition to frame separation values, the starting point (referred to as phase in wave mathematics) plays a role in finding the correct frame.

Considering again the case of 9 frames, as shown, it was the correct separation to subtract to find the maximum difference in frames since it aligned with the maximum and minimum in the waveform. Now we choose a new starting point and how it affects the results.

In FIG. 14D, we see that offsetting the starting point to frame number 5 misaligns the 9-frame separation so that it no longer lines up with the maximum and minimum of the waveform. So, in addition to doing a multitude of separations, for every separation value we also calculate the difference for multiple offsets. For example, if we difference every 5 frames, we do that difference for all offsets from M₁ to M_(n) with an increment of ΔM. An example would be subtracting every 5 frames starting at frame 1 then subtracting every 5 frames starting at frame 2 and so on. Again, in general we want the option to subtract multiple offsets in increments of ΔM within a range of A to B. For example, we may want to increment the offset 5 from 0 to 20. That would mean we do all the ranges of differenced frames starting at frame 0, then do them all again starting at frame 5 and so on.

Once we find the brightest pixels from all the calculations (both all offsets and all frame separations) we now know what pixel to look at, where the waveform starts and what the separation is of the peaks and valleys.

The next calibration we do is adapted to these values and we only calibrate based on those values.

For example, assume that we find that the peaks and valleys separation is every 25 frames, and the starting point is 5. Now we know the waveform restarts every 50 frames. So, if we recalibrate, it would be at position 55, 105, 155 . . . and so on. This eliminates the need to do all the calibrations above or what we call the initial calibration.

So, in terms of the above, the Initial Calibration is the one that does all separations and all starting points. A recalibration (adapted from the initial calibration) uses the known values determined from an initial calibration. All of these operations are conducted by the processor in a substantially autonomous manner.

Example

Simple frame differencing example using 3×3 array:

Assume we are using a camera with 9 pixels in a three-by-three array operating at 10 frames per second.

We believe the signal of interest has a frequency about 0.1 Hz so maximum and minimum values will occur at a frequency of 0.2 Hz, meaning maximum and minimum values should be about 5 frames apart. We decide to conduct frame differencing tests at 4 frames and 5 frames. Each test will calculate 4 frame differences.

To test the 4-frame possibility, we select frames 1, 5, 9, 13 and 17 for frame differencing. To test the 5-frame possibility we select frames 1, 6, 11, 16 and 21 for frame differencing.

The frames have the following values:

TABLE 1 Frame 1 3 3 5 3 3 5 3 3 5 Frame 5 3 3 0 Frame Difference 1 0 0 5 3 2 0 0 1 5 3 3 0 0 0 5 Frame 9 3 3 5 Frame Difference 2 0 0 5 3 3 5 0 1 5 3 2 5 0 1 5 Frame 13 3 3 0 Frame Difference 3 0 0 5 3 2 0 0 1 5 3 3 0 0 1 5 Frame 17 3 3 5 Frame Difference 4 0 0 5 3 3 5 0 1 5 3 3 5 0 0 5 Total Frame Dif. 0 0 20 0 4 20 0 2 20

In this test, pixels (1,3), (2,3), and (3,3) are selected as the largest pixels, each having a total time frame difference of 20 with a combined total of 20 for the three largest array values:

TABLE 2 Frame 1 3 3 4 3 3 4 3 3 4 Frame 6 3 3 0 Frame Difference 1 0 0 4 3 2 0 0 1 4 3 3 0 0 0 0 Frame 11 3 3 4 Frame Difference 2 0 0 4 3 3 4 0 1 4 3 2 4 0 1 0 Frame 16 3 3 0 Frame Difference 3 0 0 4 3 2 0 0 1 4 3 3 0 0 1 0 Frame 21 3 3 4 Frame Difference 4 0 0 4 3 3 4 0 1 4 3 3 4 0 0 0 Total Frame Dif. 0 0 16 0 4 16 0 2 0

In this test for five frames, pixels (1,3), (2,2), and (2,3) are selected as having the largest values (16, 4 and 16, respectively) but the total combined value of the three pixels is only 36 as compared to 60 in the test for four frames. So, this test would indicate that a four frame difference is the best time interval and the pixels to be monitored would be 1,3 and 2,3 and 3,3. However, similar test will be run for other phases for both the four and five frame intervals. In the next test, the four-frame interval will use frames 2, 6, 10, 14 and 18 and the five-frame test will use frames 2, 7, 12, 17 and 22. These further tests are changing the phase of the test. Assuming the next tests produce results that have lower total than 60, the first four frame test will prevail, and its “brightest” pixel locations will be chosen for monitoring.

Applicants have also tested the invention, and found that it performs well, even with asymmetric periodic waveforms. Three examples using skewed or asymmetric periodic waveforms: SawtoothRight, SawtoothLeft, and LubdubRight were evaluated as described more fully in Applicant's prior application with at least one common inventor, Ser. No. 14/757,259, titled “Method of adaptive array comparison for the detection and characterization of periodic motion”, U.S. Publication No. 2016/0217588. Each of these three waveforms incorporates a skewed 30-frame peak-to-peak periodicity evident. SawtoothRight and SawtoothLeft waveforms have a 2:1 skewed rate of falling compared with rising measurement values. LubdubRight also contains a second peak in each periodic cycle. The inventive method was able to accommodate the features of these waveforms without difficulty.

Comparison of embodiments with traditional “frame difference” methods.

It will be understood that although the invention involves subtracting pixel values at one time from those at another time, the inventive Adaptive Array Comparison method differs considerably from traditional techniques broadly referred to as “frame difference” methods in at least the following ways:

1. Adaptive Array Comparison specifically targets individual frames at particular references for the purpose of exploiting periodic signals. 2. Adaptive Array Comparison adapts to the signal, learns from the signal and modifies its approach. 3. Adaptive Array Comparison targets periodic signals to isolate them from the background. 4. Adaptive Array Comparison relates to time intervals based on signal of interest. 5. Adaptive Array Comparison isolates particular phases of motions, maximum and minimums in its approach. 6. Adaptive Array Comparison is an iterative process and involves comparison of the results of those iterative steps. 7. Adaptive Array Comparison is a temporally based and links arrays to particular points in time. 8. Adaptive Array Comparison generally involves multiple comparison of arrays over time and relies on the cumulative result.

The user interface may be configured in a wide variety of ways, as described more fully in the following examples.

Example

Because the data may be stored with the raw video on a common time basis, if an alarm sounds and everything appears to be normal, the user may simply rewind the video to review more closely what caused the irregularity, as shown in FIG. 15A-B for the related problem of respiration monitoring. The information, in the present case, might include the video, motion waveform, and a condition or quality index derived from “standard” or historical data. So, the user might press a button that rewinds the waveforms and video or goes back a preselected amount of time or to a specific preselected time and plays back the waveform and video side by side to show what triggered an event or an alarm condition, thus providing a more complete understanding of the event. Because the video is time stamped, the event of interest may be correlated with factors such as power surges or dips, lightning, temperature excursions or other environmental conditions, etc. Thus, the invention allows a more holistic awareness of the situation and makes predictive maintenance correspondingly more useful and robust.

Example

Using Waveform Signatures to Detect Events. FIG. 13A (a-b) shows a healthy waveform and FIG. 13B (a-b) shows an irregular one, again taken from respiration data. An event is clearly seen in the middle of the irregular one. Here this event will be categorized, stored, indexed and/or reported and may contribute to the condition index.

Templates may be prepared to help the user correlate an event to some known conditions. For example, a set of templates may exist including a baseline waveform for a newly installed motor or pump of a particular model and later measurements are correlated against that template to define changes, quantify wear and tear, and inform the maintenance decision-making process. The information may also be uploaded to a central database and/or provided to the maintenance professional. The information may also be included in a report. Exemplary templates may include, but are not limited to, a new machine, a worn bearing, a loose coupling, or a machine nearing its end of life.

Events may be indexed, and a single frame or video clip may be extracted that corresponded to the same time. Those compilations may be stored. One index may be targeted in particular, for example, events associated with power quality. The user may review those events to determine if an investment in power conditioning may be helpful to improve equipment life and performance.

Events may be correlated with timing through the day/night and the same procedure as described above would allow the user to determine if particular times of day/night are correlated with better or worse equipment stability.

It will be appreciated that the user interface may take a variety of forms, and in particular, the invention may be implemented in a mobile application, so that, for example, a service technician can view the data acquired at a work site and make a decision about whether a maintenance call is urgent or can be scheduled later. The technician may further have access to a database (onboard the device or on a remote server) to assist the evaluation process. The database may include historical or baseline data for a particular machine being examined and may also include comparable data for similar machines previously examined in other locations. The technician may also upload new information to the database for later analysis and comparison.

Example

FIG. 16 shows a conceptual image of a potential graphical user interface (GUI) showing a vibration waveform and video in real-time from a camera where the vibration waveform would be derived from the video. This type of interface would allow the user to view the data using a smart phone.

The camera does not have to be placed right next to the equipment. Applicant has discovered through experimentation that the inventive process is sufficiently robust that reliable data can be collected from an object in a random position in the frame and surrounded by various items, which may be stationary or might be moving to some degree. Another important advantage of the embodiments herein is that the measurement itself is not invasive or disruptive. It requires no contact with the equipment or process and no tap into the equipment's power feed.

Example

Ability to Track More than One Waveform. Conventional standoff and contact methods cannot test more than one machine at a time. The ability to do so would clearly be valuable for the typical plant environment where many different components may be located in close quarters and may each be vibrating independently.

Applicants have experimentally demonstrated that, for example, a mother and child co-sleeping were simultaneously detected, with the invention capturing dual waveforms from the same video image and displaying both waveforms simultaneously, as described and illustrated in Applicant's prior application with at least one common inventor, Ser. No. 14/757,256, titled “Method of analyzing, displaying, organizing, and responding to vital signals”, U.S. Publication No. 2016/0210747. It will be appreciated that the same approach may be extended to the factory environment.

It will be clear to the skilled artisan that the invention can be used in a factory to monitor two machines simultaneously in separate cells, in a refinery to monitor multiple valves or pumps, etc. The information may be uploaded to the cloud or to a server for continuous monitoring or, for example, to a maintenance department or field service team.

Using a normal cellphone or mobile device, the application could produce fast results for a quick assessment of a situation or to prioritize various potential maintenance jobs. Applicants have demonstrated that currently available mobile devices have sufficient computing power to do this. The method is currently running successfully on an ARM11 Raspberry Pi board, which is slower than the current iPhones and likely the 5s too. An early prototype ran successfully on the iPhone 5s using its internal camera.

Example

The invention may be configured to run on a mobile device such as a smart phone or tablet computer having a built-in camera. A user could carry the device from one area to the next, collecting video and running the analysis for many machines or process points in a plant environment. The user may acquire the video such that the image includes a fixed object or fiducial not connected to the vibrating component, thereby correcting for any shaking or instability in the handheld camera.

The video file and analysis could be stored in the mobile device or uploaded to a central server or database. At the same time, the mobile device might download other data from the server, such as historical data collected from the same machine or comparative data collected from similar machines.

Although in many examples, it is contemplated that the video image is focused on a particular machine or component under examination, it will be appreciated that the invention may equally well be carried out in a reversed configuration in which the video camera is rigidly mounted on the equipment or component and is focused on a convenient stationary object in the environment. The fixed object might be a column or other structural feature of the building, a poster or plaque affixed to a nearby wall, etc. In such a configuration, the apparent motion of the fixed object will mimic the motion of the camera on the vibrating component and the video file may be analyzed in a completely analogous manner as described earlier.

Multiple Region Perimeter Tracking and Monitoring

A perimeter-tracking approach may be used to prevent an unknown factor from entering the monitoring space of the individual machine or simply a general area. This can also be used for objects exiting the area. The user will be able to create a perimeter (via a user interface) around the area that he/she wants to monitor and does not want any intrusion into.

Multiple methods of motion detection can be used in the perimeters. For example, a technique such as adaptive array comparison can be used to see if changes have occurred around the perimeter from one frame of the video to the next.

Another technique may be comparison of frame intensity values in the area of interest. Regions can be selected, and those regions summed for a total intensity level. If that intensity level changes frame to frame by a certain threshold, motion is determined to have occurred.

Blob comparison may also be used for motion detection.

Single pixel edge motion may be used. It will be possible to determine the perimeter with great accuracy based on movement of an edge of a single pixel or virtual pixel, which will allow for a much greater degree of accuracy compared to using conventional blob techniques. The area being selected does not have to be a series of large boxes as in current technology but instead can be any sort of perimeter that the user chooses to select. This could offer the ability to use a very narrow single pixel perimeter or single virtual pixel comprised of multiple pixels.

Feature tracking may be used by locating features in the perimeter and tracking their location in the perimeter. If their centroid location changes then motion is detected. Correlation of a selected number of pixels with a feature in them can be correlated to sub-regions in successive frames to determine if the highest correlation of the original set of pixels is correlated more highly to another location other than the original location.

It will be understood that there are several factors that could create false readings, including but not limited to outside factors such as wind from the outdoors, vibration from another device in the room, movement of a curtain or other object in the room, or latent movement from someone near the subject. To help factor out these false positive readings Applicants contemplate the use of various techniques to isolate targets of interest.

Example

The invention may be installed and used in conditions where there are multiple regions to isolate, such as a busy production line involving numerous work cells, individual robots, etc. Each area can have separate perimeter monitoring using perimeters drawn by the user via the GUI.

Isolation of Frequency:

Applicants have also recognized that the invention may further use frequency isolation and a learning algorithm to learn the likely machine vibration rate and distinguish it from outside factors that could produce a vibration or movement in the field of view of the camera. This will help distinguish movements in the field of view (such as a fan or wind blowing a curtain) from movements associated with vibrations of interest.

Motion may be allowed inside the area of interest without alerting or affecting the monitoring of the perimeters. This would allow for an object to freely move within the area of interest, for example a welding robot, but still allow for monitoring of the perimeters.

An object detected moving in the perimeter can be characterized by the number of regions in which its motion is detected to give an estimate of size. The time between detections in various blocks can give information as to speed based on the known physical projection in space of each pixel. The series of blocks through which the object is detected to be moving can indicate the direction of travel.

Motion can be detected through the inventive method of array comparison of different frames. Frequencies such as fast-moving objects can be filtered out by comparing frames with larger separation in time, and slower frequencies or a slower moving object can be filtered out by comparing frames with shorter separations in time. Thus, the invention can be used to isolate certain motions for detection or rejection.

Using light level changes to detect motion can cause false a positive indication of motion from things that change the illumination of the scene but are not objects moving in the field, such as fans or curtains moving from air flow. Comparing different separations in frames (hence different separations in time) can eliminate these spurious indications. For example, the slow light level changes from the natural daylight cycle would not be detected if a short time separation in frames are compared.

The invention may further include a method for determining, comparing, measuring and displaying phase, which is of particular relevance for the case of machinery.

It has been shown that intensity changes over time can be detected and correlated to physical phenomena. In many cases those signals may appear to be periodic. The periodicity can be described by frequency, amplitude, and phase. In addition to the frequency and amplitude, phase is an important characteristic of the periodicity that helps temporally describe the signal and also describe one signal relative to another and relate those signals to patterns of repetitive events such as periodic motion.

The following example describes a method for extracting and analyzing phase information from time varying signals. This may be done on a single pixel level and/or for a plurality of pixels. The phase information is shown and displayed in numerous ways. Information can be gathered from the time varying signal based on the phase and its relationship to other parameters.

Example

Simplified Explicit Stepwise Procedure:

1. A time varying signal is sampled in time with a photo detector, transducer or other suitable device. This sampling represents a time sequence with appropriate resolution to correctly sample the signal of interest. 2. Multiple samples can be collected simultaneously with a plurality of pixels, e.g., with a video camera where every pixel in a single frame represents a sampling at the same point in time at different spatial locations in the scene. 3. The resulting sequence is an array of X Y x t where X represents a spatial dimension, Y a spatial dimension orthogonal to X, and t represents time. 4. FFTs are performed in the time domain along the t axis for every pixel or element in the array. The FFT then returns a frequency spectrum for each pixel along with the amplitude and phase for each frequency. 5. The phase information for each frequency can be displayed. For a given frequency, a phase reference such as 0° may be arbitrarily selected or may be associated with trigger, pulse, absolute reference, specific sample, or frame as may be preferred or selected. 6. To create a phase mask image we plot a representation of phase for a given frequency in the same pixel from which it was measured. To create a two-dimensional image, we first set the frequency we are interested in. For example, we may want to see the phase relationship for the 30 Hz signal. To do this we filter the image so that pixels that are in the selected phase range are white (represented numerically as 1) whereas all others are black (represented numerically as 0). The phase range may vary but for this example we will use .+−0.5°. For example, if we select 30 Hz and 55° then the image will show white (or 1 numerically) where a signal exists that has a frequency of 30 Hz and has a phase from 50°-60°. This has the benefit of showing all elements of the scene that are in phase at the same frequency as they all appear white while the rest are black. 7. Taking this a step further, one can hold the frequency constant while adjusting the phase to 235° which is 180° out of phase of 55°. In mechanical systems, misalignment is typically 180° out of phase across a coupling. In this manner it is possible to look at two different phase values to see if there is a phase shift indicative of misalignment. Another example would be to look at a structure such as a bridge to see if structural elements are moving in or out of phase. 8. Now if one were to start at 0° and toggle to 360° one would see all the different locations of the different phases for the 30 Hz signal. They would be indicated by the fact that the pixel turns white. 9. This entire process can be repeated for every frequency.

FIG. 17 outlines one approach for computing and displaying phase.

It is possible to use intensity readings to increase the information in the phase images. For example, one could take the intensity of the frequency at each pixel and multiply it by the phase mask image. Since the phase mask image is binary (if the signal is at a particular phase it is white, or valued 1, and if it is not at the selected phase it is black, or 0) the phase image acts as an image mask that will only allow the intensity values to pass if it is at the selected phase. All others will be zero. If it is in phase the intensity is preserved since it is multiplied by 1. This will create a scaled image that shows only things at a given phase and what those intensities are.

Example

Use of phase imaging is illustrated in FIGS. 18A-C.

FIG. 18A shows a single image from a video sequence of a bridge. During this sequence a vehicle passed over the bridge (not shown).

FIG. 18B shows a single-phase mask image depicting a single phase (that of the fundamental vibration of the bridge) at 2.25 Hz, the bridge fundamental frequency. In this image things moving in phase show up as white (value 1) whereas things that are out of phase show as black (value 0). The image is scaled such that 0 is black and 1 white. One can see that the motion on the I-beam support shows a clear feature of motion indicating the entire span is moving in phase with itself, as one would expect.

FIG. 18C shows an image of the phase mask seen in FIG. 18B multiplied by the intensity at each pixel of the amplitude of the 2.25 Hz signal, which relates to motion. One can see that now the phase image is scaled with relative values. Furthermore, the image is much cleaner as small amplitudes of frequencies can be set below a threshold using the noise reduction technique.

If the amplitude of the frequency of interest due to intensity changes is calibrated to a particular value, then the phase mask image (that is composed of 1s or 0s denoting in or out of phase respectively) can be multiplied towards a calibrated frequency amplitude image or array. Then the resulting image displays only things in phase at a particular phase of interest at a given frequency and offers a calibrated value. That calibrated value may be from anything that is causing the signal levels to change. It could be temperature variation from thermal IR imagers, displacement from moving features in a visible image or even variations in absorption levels through a transmitted medium.

For a measurement made with video imagery the phase may be referenced simply to the first image taken so that all phase readings are relative to the first image. However, it is possible to synchronize the phase readings to another signal. This could be a trigger pulse or even a time varying optical signal in the scene of the imager.

Exposure modes on imaging sensors are often different. Two types of modes are global and rolling shutters. Global shutters expose every pixel at the same time. Rolling shutters expose lines of the sensor at different times. In the case of a global shutter all pixels are exposed simultaneously so the phase relationship is preserved across the sensor. In the case of a rolling shutter there are variations in the timing of exposure from pixel to pixel. It is possible to realign temporal signals based on the known delay between pixels after they are read from the imaging sensor. By accounting for this offset we can preserve the relationship of phase across all pixels.

It is possible to use the phase information in a noise reduction manner. For example, in the event of a phase image mask where the array or image is binary (1s for in phase, 0s for out of phase) one can reject all pixels out of phase at a given frequency and given phase. When exploring an image, if many pixels effectively “turn off”, it eliminates much background noise in the scene and makes detection much easier. This may be advantageous, for example, in a cluttered field or where many time-varying signals exist. Additionally, one can reduce noise by multiplying the phase mask image by the frequency intensity image and setting an intensity threshold below which the pixel is set to 0 or not represented in the scaling.

Mechanical or anelastic properties that have particular phase properties can be imaged and detected with the described technique. Phase relationship information can be exploited with the described technique to reveal physical parameters or other properties.

By cycling through all the phase mask images at a given frequency, traveling waves may be seen in the sequence of images created.

Different areas of the array or frame of the same or different phase mask images may be compared to show certain areas of interest indicating anomalies, e.g., one area that is out of phase with the rest. Or these areas could be compared to find patterns indicative of physical phenomenon.

The following exemplary cases demonstrate some useful applications of this aspect of the present invention.

One use of phase presentation as described herein is to determine and to graphically display absolute or relative timing characteristics and patterns.

A second example is to demonstrate a modulation or a beat frequency or other characteristic which may correspond with a movement of an object of interest.

A third example is to represent a leading or a lagging event sequence made evident mathematically or graphically using techniques described herein. Again, this leading or lagging event sequence may be related to a movement sequence of an object of interest.

A fourth example of the present invention is to characterize highly repetitive displacement patterns such as a static or a dynamic constructive and destructive interference pattern resulting from multiple vibration wave fronts. The multiple fronts each typically originate from a point, line, or area of reflection, or originate from a point, line, or area vibration energy source. This technique may be used to discern false or positive indications. For example, a false indication may be found from a highly repetitive pattern which is more likely produced by a machine than a living being.

Method of Motion Amplification

Another method that can be used to further enhance the inventive technique is to amplify motion by an amplification factor. This factor determines the strength of the overlay of the difference image sequence on top of a static image from the original motion sequence. For example, if the factor is 1 there may be equal weight applied, whereas if the amplification factor is 30 the difference frames are increased in intensity by some factor relating to 30 in the composite image. This would allow one to determine how much of the difference sequence is present and how much the static frame is present. A factor of 0 may turn off the motion and just show the static image.

Example

FIG. 6 shows the original image frame of a paper square with a plus sign, attached to a speaker vibrating periodically. The arrow in FIG. 6 shows the motion of the paper relative to the frame imaged by the camera. This image corresponds to what is being imaged in FIGS. 7 through 12.

FIG. 7 shows a difference image in a sequence from the median reference frame. Notice the increased intensity change on the top and right side of the paper with the plus sign.

One can see that in FIG. 8 the increased intensity change is now on the lower and left side of the paper. This is from a frame in the same sequence at a later timestamp. Comparing FIGS. 7 and 8 indicates the motion is up and to the right and then down and to the left. FIGS. 9 and 10 show the same behavior except that amplification has been applied to these frames, meaning there is an overlay to the original images such as the one shown in FIG. 6. The amplification factor determines the relative contribution of the original image such as FIG. 6 and the difference portion such as in FIGS. 7 and 8. More of the spatial detail of the scene is present in FIGS. 9-10 because overlay of the original image in the original sequence contributes to the image displayed. FIGS. 11-12 illustrate similar results using difference frames calculated using reference frames other than the median frame.

The following explanation will further illustrate the conceptual basis for the inventive motion amplification process:

Example

Amplification works by amplifying the changes in the images that are associated with motion. Often an edge or feature in an image is not ideal, i.e., the threshold or contrast change does not fall on one pixel. Although we present a non-ideal case, amplification would work equally well in an idealized scenario. This would be the case of a larger pixel or high-quality optics to put the edge solely on one pixel.

FIG. 20A shows an idealized edge at the top, transitioning from a high to low value left to right. The center solid line shows the non-idealized case, more commonly occurring due to irregularities at the surface of the target, degraded image quality from optics or variations resulting from a turbulent light path, to name a few. Also shown in the center is a dotted line indicating the motion of the edge feature moving to the right.

At the bottom of the figure are two rows of pixels showing the intensities of the pixels from imaging the edge. The top row of pixels shows intensities of the edge before it moves while the bottom row shows intensities after it moves. Note the overall brightening of the three center pixels as these portions of the image are imaging the edge detecting variations associated with it moving. Also note the left and right most pixels don't seem to change as they are not imaging the edge.

FIG. 20B again shows the motion of the edge towards the right of the image. Again, we see the before and after effect on the pixels in the top two rows of pixels. The bottom row of pixels shows the numerical change associated with each pixel as the edge moves right. This is the difference value of those pixels. This value is the value that gets amplified. For example, if we amplify these values by a factor of 10, the +2 becomes 20, the +10 becomes 200, etc.

FIG. 20C shows the same effect as FIG. 20B except the motion is now to the right and more of the dark side of the edge is imaged. Note the pixel intensities darken where portions of the edge are imaged and the values in the bottom portion of the figure denote the integer values of pixel changes decreasing.

These amplified differences are then used to create the amplified motion sequence. Note we may add these amplified sequences to the original video sequence to increase the effect of seeing what was in the scene before amplification or we may choose to visualize only the amplification sequence.

Applicant has discovered that this method can be further modified to create the appearance of exaggerated movement in the image, by superimposing the differenced frame onto the reference frame and using the resulting image to replace the original frame in the video.

Example

The first step is to choose a reference frame. In the case of periodic signals, the median works best because that is the zero value. (The median image is created by calculating the median value of each pixel over the length of the video and using all those values to create a median image.) Then every frame is compared to that reference frame to create and new set of differenced images. If the video clip is 800 images long (10 seconds at 80 fps) this will comprise 800 difference frames. These images will likely show relatively small variations in pixel intensity because that is only what has changed.

The next step is to amplify. For example, if we want a factor of 30 amplification, we multiply all the differenced frames by 30, so if one particular pixel in a difference frame has value 2 it becomes 60. This creates a new set of differenced frames that are amplified by the selected factor.

Those amplified difference frames are now directly added to the original frame. This helps to introduce a semblance of the original scene and give a baseline. (Note that 8-bit images are only 0-255 so because we added to the original image, we may need to rescale to make sure the intensities fit in 8 bits. This will introduce a small amount of noise into the image, as seen in FIGS. 19A and 19B, but does not interfere with the added functionality of being able to clearly visualize the motions.)

Now we have a new set of images that comprise the original image plus the amplified difference frames that represent motion. These new frames are put back in the video and played back. The frame rate is generally reduced so the motions are easier to see, especially if the motions occur at high speed.

Example

A short video was taken of a small electric motor coupled to a supported rotating shaft having a flywheel centered between the supports. In the raw video, some eccentric motion can be seen in the coupling between the motor and the driven shaft, but the motor itself appears to be motionless. However, after applying the motion amplification procedure described above, the movements become clearly visible so that one can see exactly how the motor is moving or rocking during operation. Careful examination of two still images, FIG. 19, shows a visible difference, with the apparent position of the motor slightly more parallel to the ROI box in FIG. 19A, and more skewed in FIG. 19B.

When viewed as a video, the visual result is not only striking, but in many ways completely surprising, as there are no additional steps or mathematical modifications to cause the apparent motions to be amplified. The process is actually targeting motions that are subpixel, in many cases much less than a pixel. The process for creating the amplified motion video simply alters the individual pixel, in other words a measurement from one pixel isn't directly altered or translated to a neighboring pixel to make it look like the edge moves into that pixel. In most cases, defocusing and other issues often cause the light in about 4 pixels to be changed by an edge motion so each one of those respective pixels' motion is amplified and causes the motion effect to be present in all of them.

Applicant speculates that one phenomenon that might be at work here is that multiple pixels are behaving in a correlated way. In other words, when an edge or feature moves one sees the effect of motion in multiple areas and visually processes that as motion. For example, with the rocking of the motor, one sees the entire side of the motor go up, so all of those pixels are working together in a correlated way to make the viewer perceive that the object is moving.

Applicants note that although the motion amplification process has been descried with particular reference to periodic movements, the principle may equally well be used to amplify movements associated with non-periodic and transient events. Also, the motion amplification does not have to be “played back” in the traditional video sense or in the form of a movie file per se. It can be used in an interactive piece of software enabling the user can go backwards and forwards from frame to frame in any manner desired. One example would be to have a slider on the GUI, with which the user, by moving it back and forth, would control what frame is displayed.

Example

Thresholds and limits may be set for the entire differenced image or certain regions for a computer system to autonomously make decisions without human interaction. If certain pixels or groups of pixels behave in certain way, then action by the computer can be taken without human interaction. An example of this may be intensity level changes indicative of motion in a certain direction, motion above a certain threshold, or motion of specific components being present. These are a few examples of a multitude of events triggering a reaction. Events could trigger a reaction or action in correlation to an outside event that is inputted into the system from an outside source.

The information gathered from this system could be used to control outside systems, for example, a feedback response control, alarming system, or process control. A multitude of outside systems could be integrated with the system.

Application to Process Control

As noted earlier, various components may exhibit motions even though they are intended to be substantially stationary. For example, pipes and ducts may vibrate or experience transient motion in response to something changing inside (pressure, density, temperature, fluid cavitation, “water hammer”, etc.). Vibrations may be characteristic of other changes, such as the buildup of coke in refinery equipment, clogging or bridging in cement kilns, or sudden changes in the feed material in a mill or classifier. Applicants contemplate that the invention could be applied to such problems in cases where experimentation can definitively calibrate particular external movements with particular internal process attributes.

Application to Quality Control

Applicants have found that the invention is able to detect variations over time from motion across a contrast change with a high level of precision. In some cases, this is sub-pixel motion. In the case where unwanted defects or non-uniformities exist, Applicants have discovered that the system may be used to detect those regions as described in the following Examples.

Example

An object might have a small flaw in the surface that is very difficult to see by simple visual inspection. To enhance the detection of this flaw, the object may be vibrated at a particular frequency while a camera views its surface. That frequency would preferably be a known frequency selected to be easily detected by the camera operating at a known frame rate. The system would then highlight motions related to that frequency, i.e., the image of the flaw moving back and forth at the vibrating frequency imposed on the object. The result would be any region in the scene with a contrast change would be detected when the object is vibrated. Areas of high uniformity would go otherwise unnoticed by the system. Thresholds may be set to determine whether a detected signal is considered a positive detection.

Example

A painted object that is supposed to have a highly uniform finish would be particularly suited for this type of inspection. For example, in auto manufacturing, exterior components such as doors, must be inspected for irregularities in the paint job. An inspection system could bring the door in front of the camera. The door would then be vibrated at a convenient frequency, e.g., 10 Hz. The system then isolates the 10 Hz motion. The system analyzes the 10 Hz image looking for regions in which a detection of modulated signal exists. If the detection occurs and it is above a certain threshold the system identifies a defect.

It will be appreciated that such an inspection process is inherently faster, more accurate, and more consistent than human visual inspection.

Application to Alignment

Aligning a shaft or other piece of equipment is a critical process that is needed from time to time or on initial installation of equipment. The alignment is necessary for properly maintaining function of equipment. A misaligned shaft can cause unnecessary wear, improper production of product and reduce equipment life. The inventive methods can be used to properly align equipment. A camera can be placed on one side of the alignment location, perhaps on one side of a coupling. The camera may look at an existing edge or perhaps a calibrated edge placed by a user. The camera can then monitor that edge and make displacement reading on the edge to determine positional changes over various operating conditions. That information can then be used to determine the proper alignment positions. In another instance a second camera may be placed on the opposite side of the alignment location, so the cameras look at either side of the alignment location.

The cameras may be positioned so they can rotate with the shaft to measure different orientations. In another instance one or more cameras may be placed away from and not on the equipment to measure the displacement of the shaft from a distance in one or more directions.

Alternatively, a camera may be place on one side of the shaft. The camera may track the position of the shaft at one location in space as the shaft rotates. This would effectively measure the shaft orbit and describe the current alignment of the shaft. These values can be used to align the shaft.

This can all be done while equipment is in operation or off. This would allow for operational situations such as thermal expansion to be considered.

Graphical User Interface for Motion Amplification

It will be appreciated that many of the computational operations in the motion amplification process may be run autonomously, where the computer applies a set of default values, e.g., amplification factor, playback speed, etc. This may be particularly useful in cases where the same or a similar component(s) are to be examined periodically. Alternatively, in other situations it may be desirable provide, via a GUI, the ability for the user to adjust parameters of interest. The following examples will illustrate some of these aspects of the embodiments herein in greater detail, to underscore the wide range of embodiments and alternatives provided for.

Example

A GUI showing some features applicable to motion amplification is illustrated schematically at 211 in FIG. 21. Image box 212 may display the motion-amplified video file or a portion thereof, running at a selected speed or frame rate, or it may display a single frame, depending on the user's selection. Playback speed may be controlled via slider 213, over a selected range from 0 (i.e., single frame) to 100% (i.e., the speed at which the raw video was originally acquired). Playback speed (fps or % of original) may be displayed in box 217. If a single frame is displayed, slider 214 may be used to select a particular frame for display, and the time corresponding to that frame may be indicated in box 218.

The user may select a motion amplification factor via slider 215, if desired; a button may be provided to allow the user to select a default amplification factor. In either case, the amplification factor being applied may be displayed in box 216.

Image box 212 may further allow the user to select a region of interest for processing as described in Applicant's co-pending application Ser. No. 14/757,255. Image box 212 may also display a computed map of the deviation of average motion from the mean, analogous to the data display shown in FIG. 4. The GUI may further allow the user to export the processed video (e.g., video with selected amplification factor and selected playback speed applied), along with associated metadata if desired, and this button 220 may further provide a conventional dialog box for the user to choose destination, file type, file name, etc.

Example

FIGS. 22A-C illustrate three (of many) possible sequences that may be selected by a user via the GUI. In FIG. 22A, the processor selects a reference frame, applies a default amplification factor, and the user exports the processed video. In FIG. 22B, the user selects a desired amplification factor (e.g., via slide control 215) after which the processed video is exported. In FIG. 22C, the user selects a desired amplification factor, and the amplified video is displayed but not necessarily exported. This mode is particularly useful in cases where the best amplification factor isn't known a priori, so the user might try several values and then select the best processed file for export.

The following examples will illustrate various ways in which the invention may operate autonomously or with varying degrees of user input.

Example

The inventive system may produce motion-amplified video from raw (source) video without any user interaction. In this instance the user may select a video, or the system may continuously monitor for new video. The processor than processes the video to amplify motion and outputs the motion-amplified video. The processor may amplify motion based on a default setting or from values determined from the video itself. Examples may include, but are not limited to, motion levels, displacements, and ambient light levels.

A familiar structure that is subject to relatively uniform repetitive loads is the trackway of a roller coaster. Such structures are particularly amenable to autonomous or semi-autonomous monitoring. A video camera may be positioned to view a critical structure to observe deflections when the cars are passing over it. A separate triggering system may activate the camera for a fixed collection period (say, 20 seconds) each time the cars are approaching. Alternatively, the video acquisition may run continuously, and the computer may detect the approaching cars in the image itself and trigger the system to save the next 20 seconds of video. The computer may also apply a preset amplification procedure using default values and save the processed video. The saved file may later be compared to those of other times to quantify structural changes. Alternatively, the processor may trigger an alert condition if the amplified motion exceeds a predetermined threshold.

Example

The invention may allow a user to specify a subset of source video for motion amplification. This could be done graphically based on motion pulled from the video. For example, a nominal motion plot (average motion level y vs time x) may be displayed, and the user would choose a subset of x-axis or time. For example, the GUI might show a nominal motion plot over time for the roller coaster. It would typically be flat until roller coaster cars come into view and then flat again after cars leave frame. The user could easily tell when video should start/end from this plot. A thumbnail/sparkline plot instead of a plain slider may be used to show current location. Thus, the invention may use average motion level plot as a navigation mechanism.

Example

The motion amplification factor may be automatically adjusted based on motion levels seen in the source video. The program may automatically determine motion levels and find a suitable amplification factor, after which the user can decide if it is acceptable or alter it if desired. Alternatively, the user may choose to accept defaults and let it process automatically.

Alternatively, the user may not interact at all and the software may process video autonomously without a display or human interaction. It may process a series of data sets to produce a series of motion amplification videos.

Alternatively, the user may select a preset value or fine tune a value based on one video and then process a series of data sets to produce a series of motion amplification videos.

Example

The user may have the ability to let the processor adjust amplification level in the output video. In some instances, too much amplification may wash out highs or lows in the video. The user may have the ability to detect that a specified amplification level is washing out highs/lows automatically by having the GUI report this to the user.

Example

The processor may automatically determine which pixel(s) to amplify. For example, if the motion is below a certain threshold the processor may choose not to amplify that pixel. This could help to reduce some noise in pixels that don't show much motion but only amplify noise. The user may also be able to choose that threshold, and further be able to play back the amplified video based on the selected threshold and have the option to change it. The threshold may be adjusted in essentially real time while the video plays back.

Example

The GUI may allow the user to specify a region of interest of source video for output video:

1. The user may have the option to graphically select a region of interest to process, e.g., by drawing a box or ROI. 2. The GUI may show an x-y plot where each x-y location shows the deviation of the average motion level from mean, allowing the user to easily see which region should be the focus for the output video. This would act as a sort of an x-y “heatmap” for motion.

Example

The GUI may further include a second image box 219, in which the raw (unprocessed) video or single frame is displayed that corresponds to the processed image shown in image box 212. This feature gives the user the ability to make a side-by-side visual assessment of the effectiveness of the selected motion amplification parameters. The user may also choose to export the original video file along with the processed (motion-amplified) video file.

Example

The user may optionally choose to add some color to regions that are amplified. Instead of changing the original video of R1, G1, B1 (R1=G1=B1 for the case of mono video) to R2, G2, B2 during amplification, color may be added or overlaid, for example some extra red or just the addition of red or the addition of red and subtraction of green and blue.

Example

The processor may tint pixels (e.g., add red proportionally) based on deviation of motion level of each pixel from the mean.

Example

The user may have the ability to specify a desired frame rate of output video, e.g., via slider 213; however, the program may suggest a frame rate based on motion frequencies found in data and a predetermined rule set. For example, the motion needs to be perceptible to the eye. A vibration at 60 Hz, e.g., may need to be slowed down so the user can see the motion. The processor might use a preset criterion, e.g., to make the motion about 5 Hz or 15 Hz, based on optimal viewing conditions. Then the processor may determine which frequencies are present or which frequencies are of interest and then slow the video by an amount such that the motion is displayed at the optimal frequency for viewing. As an example, if a 60 Hz machine is of interest for 1× running speed, then to get it to 15 Hz one would use a 4× slowdown so on a 120 fps camera we output the video at 30 fps.

It will be appreciated that the invention may be used interactively with maintenance, repair, or other corrective activities. In this situation, the user might collect video files before and after a corrective action. The motion-amplified files may then be aligned in phase with one another to show a change or delta in the motion levels. Conversely, two files taken at different times (e.g., monthly, quarterly, or annually) may be compared in order to highlight possible wear and tear, structural fatigue, deterioration, or other source of damage.

Motion amplified frames may be used as a navigation mechanism to construct vibration plots. For example, the user may use a single or multiple frames in the amplified data to select a single pixel or region of interest. A time waveform, frequency spectrum or phase information among other things may be extracted from this location. The location may add context to this data. Alternatively, the system may automatically determine these regions and export the excised data from these regions. This data may be intractable. For example, in the event of a time waveform the amplified video may playback and update the location in the time waveform corresponding to the frame or point in time in the amplified video. Or perhaps the time waveform traces out in time as the amplified video is played back. The user may grab a cursor on the time waveform and the cursor on the waveform adjusts the playback of the video. This data may be displayed as its own window or in the same window or overlaid on the amplified video. The exported video may have overlaid data that updates as the video plays back. For example, an overlaid time waveform of a certain region(s) may be overlaid on the amplified video and updates a cursor showing the given point in time of the video on the waveform. The user may preselect one or more regions of interest or “virtual sensors” at the time of acquisition or other given time and that information may be loaded in the metadata of the stored file along with the vibration and video data. The user may also choose to input other metadata such as fps, lens focal length among others to be stored with the video data or separately. The user may also add virtual sensors or ROI measurements as different times. Each time a measurement is made that info may be recorded and the metadata and displayed automatically. This may make analysis faster in the future if measurements are already present. The user can then sort measurements by time, user etc.

Example

The system may be used in real time. The system may calibrate for a period of time to determine medium frame of reference for motion. Amplification may be done in real-time as frames come in. That information may be displayed or not displayed. Certain actions may be made by the processor based on that data even if it is not displayed. Amplified data may be streamed, stored on disc, or discarded.

Example

A database may be established and maintained, from which a user may select an asset and select the raw video for that asset corresponding to a particular time.

Example

The processor may have the ability to synchronize a plurality of cameras to amplify motion in all or a selected number of cameras. The program may have the ability to synchronize with other cameras where one camera provides video footage for motion amplification and other for displacement measurements as taught generally in Applicant's co-pending application Ser. No. 14/757,255.

Example

Some other features that may be provided include the following: 1. The processor may have the ability to select a single frequency or multiple frequencies to pass or reject though filtering. 2. The processor may have the ability to automatically create amplification video through default settings or measurements such as displacements and frequencies in the video to determine amplification levels. 3. The GUI may provide for the user to mouse over a point and show displacements in a popup window based on measurements of the data. 4. The user may upload raw video to the cloud, and have the motion amplification processed on another computer, which would then return the motion-amplified video to the user.

Example

The video file(s) that are input into the inventive system may be acquired from any selected source. In particular, there is no requirement that the video files be created specifically with the invention in mind. As an example, archival video footage of an earthquake, perhaps inadvertently taken by an amateur or by a news crew, may be analyzed to visualize the motions of buildings, bridges, or other structures. Video may be routinely collected of aircraft taking off and landing at an airfield or on an aircraft carrier; if a particular aircraft later has a problem, video of its recent takeoffs or landings may be retrieved and examined. In cases where forensic analysis is being done, authorities might solicit raw video from the public and thereby obtain video files that could be analyzed to better understand an occurrence such as a structural failure, accident, bridge collapse, etc.

It will be further appreciated that although the invention is described as non-contacting, in the sense that it is applied strictly to images and doesn't rely on physical contact with the component in question, the invention may be used in conjunction with established test procedures that do involve some physical contact during the course of the test, as described in the following example.

Example

Structural cables are well known and used, for example, in suspension bridges. Structural cables in the form of guy wires are used extensively to maintain the stability of tall, lightweight structures such as antennas and towers. Maintenance procedures require the tension in such cables to be measured periodically, and this is typically done by evaluating the resonant frequency of the cable. Although in some situations there is enough natural resonance arising from wind or other loadings, resonance is often excited by physically striking the cable with a heavy tool or bat. Depending on the geometry of the structure and the height above ground at which the worker must strike the cable, such tests can be hazardous. Using the invention to amplify motions, for more accurate characterization of the vibrational frequency, may reduce the hazards by:

1. Making the measurement sensitive enough to eliminate manual excitation and rely completely on natural movements caused by the wind; 2. Allowing the cable to be manually excited from a position nearer to the ground; or, 3. Allowing the striking tool to be smaller and lighter, so that it can be operated with one hand by the maintenance worker, allowing him/her to maintain “three-point contact” with a ladder, bucket, or other support.

Example

The invention may be adapted to the analysis of guy wires using the following approach:

Develop a processing routine that relates the determined vibrational frequency to cable tension and make this available via the GUI, so that a user can simply mouse over a particular cable and that cable's natural frequency and/or tension will be displayed. Note that an industry-approved frequency-to-tension equation exists for the case of cell and radio towers. This identification of cables may be done automatically so that no user intervention is required. The user may simply acquire the video and the system then displays the cable tension. For example, the system may automatically special isolate the cable in the imagery data to separate them, so the user does not have to click on each cable.

Collect a video file of the entire tower or a portion thereof in which all guy wires can be viewed.

Analyze the video and display the amplified image on the GUI. Then, click on each individual cable and determine its properties. Note that in some cases, it might not be necessary for the processing routine to calculate actual tensions, as it will be appreciated that if the user determines that one of four nominally identical guy wires has a significantly different resonant frequency from the other three, this fact alone might be sufficient to identify a need for maintenance work.

Example

The invention may also analyze the ambient light, often seen at 120 Hz, and align the acquisition frame rate to the light frequency as well as phase align. It may also increase or decrease the exposure time to account for the ambient light oscillations. For example, the light might be flickering at 119.9 Hz, in which case the processor could change the frame rate to match.

Example

As noted earlier, Applicants contemplate that the motion-amplification may be done more or less autonomously, with the processor selecting the amplification factor and the playback speed so that the motion will be readily apparent to the naked eye. In general, when a reference is made herein to a processor performing a step, it will be understood that in the absence of a specific statement to the contrary, the processor is either performing the step automatically (autonomously) or the processor-related step is user-directed, such as through a user interface. It further will be understood that such a processor is executing machine readable program instructions, referred to at times herein as “software” or “code.” As an example of the versatility of this approach, a user could download an application to a smart phone, e.g., and then:

1. take a video; 2. upload to a cloud-based server running the motion-amplification process; 3. download the processed video to the same smart phone or have it exported elsewhere for later viewing.

In this mode, the user is simply a customer of the service and the entire processing operation is autonomous and essentially hidden from the user.

The following examples will illustrate ways the embodiments herein may be used to evaluate phase, as components in motion show contrast to other objects in the recorded scene while moving, for example, from a light environment to a dark environment as they are being recorded. An objective is to normalize the study of dynamic motion in components based on their actual phases of motion captured in a video recording. For ease of reference, herein lighter is referred to as “white” and darker is referred to as “black” consistent with the phrasing typically used in the relevant field. Also, for ease of reference, the term component in the following examples may refer to one component, or refer to an entire machine, or to multiple components (if stated as plural, components) of a machine.

Example

Each pixel in a video recording can be considered as a separate sensor, and the phase associated with a given pixel may be influenced by and/or correlated with an edge or a direction of contrast in light at the particular location of a component represented by that pixel. Consider a first edge of a component that transitions from white to black represented by a first pixel, and a second edge of the same component that transitions from black to white, represented by a second pixel. These two edges may be in phase with each other when it comes to the directionality of their motion or areas of contrast each is undergoing, yet still have their phases 180 degrees apart if one measures the modulation of light at those pixels. Even though the edges are moving together, one pixel will dim while the other brightens.

FIG. 23 is illustrative. The Figure represents one component 230 with two white squares (231, 233) and two black squares (232, 234) representing individual portions of that component. The four portions are moving together as a single entity such that the entire component should be considered in phase. However, if one measured the modulation of light associated with the two vertical interior edges marked by the dotted circles while the component is moving periodically right to left, the vertical edges would produce a time series of measurements that was out of phase with the actual motion of the component. Similarly, if one measured the modulation of light associated with the two horizontal interior edges marked by the dotted rectangles while the component is moving vertically, the horizontal edges would produce a time series of measurements that was out of phase with the actual motion of the component.

Consequently, an attempt to correctly measure the phase of the component at the frequency of the periodic motion would create a misleading understanding of the phase relationship between the four portions of the component based on the identified interior edges. Specifically, the portions would appear to be moving out-of-phase with respect to each other, when in fact the entire component is moving together as a single entity. If two accelerometers (241, 242) are mounted on opposite surfaces of the same object (240), represented again in FIG. 24, this same phenomenon occurs when making point vibration measurements with the accelerometers. As a matter of course, the accelerometers will measure out-of-phase readings because the sensors (241, 242) are mounted with their orientations out-of-phase. To get consistent readings, when the information is processed by a device 245 with a processor, which is operatively connected to the accelerometers, 180 degrees must be added to the readings of one of the sensors. When the sensors involved are relatively few, the necessary corrections can be observed and easily applied to obtain the correct result. To remove the phase confusion when creating a phase map for an entire scene or for subset of the objects in the field of view, however, which may involve a video recording device that has millions of sensors, the phenomenon cannot easily be handled by manually applying a correction to the phase calculated at each pixel. Rather, a mathematical algorithm is needed to process the video before any phase values are determined.

If one measures phase and associates that measurement with motion due to contracting areas of edges moving, a normalizing phase adjustment may facilitate a comparison of areas with respect to directionality on contrast or phase. It is well understood that the edges or contrasting portions of an image can be determined. Examples of known ways to determine edges include methods such as canny edge detection and gradient operators like the Sobel operator. An example of a Sobel operator is:

$L_{x} = \begin{bmatrix} {+ 1} & 0 & {- 1} \\ {+ 2} & 0 & {- 2} \\ {+ 1} & 0 & {- 1} \end{bmatrix}$ $L_{y} = \begin{bmatrix} {+ 1} & {+ 2} & {+ 1} \\ 0 & 0 & 0 \\ {- 1} & {- 2} & {- 1} \end{bmatrix}$

Where L_(x) and L_(y) are the Sobel operators of the x and y directions, respectively. By using a directional operator, the directionality of the edge can be determined for both horizontal and vertical edges. Note this operator is not the only function that can be used to measure and ultimately normalize edge directions, i.e., phase. This operator is provided as a non-limiting example, but skilled artisans understand that a normalization of edges and directionality can be performed though other mathematical operators.

In some embodiments, an algorithm using the Sobel operators is employed to perform a normalizing phase adjustment in a video recording is provided in FIG. 25A-B. A frame of the video is selected in step 310 and the application of the Sobel operators occurs in step 320 producing two image frames that represent the horizontal and vertical edge strength.

Besides directionality, the strength of an edge can be determined by various methods as well. One result will be an edge image, where each pixel's magnitude is proportional to the strength of the edge for that pixel location. If the operator used is a vertical edge detector, then the result is an image showing the strength of each pixel's edge in the vertical direction. Likewise, if the operator used is a horizontal edge detector then the result is an image showing the strength of each pixel's edge in the horizontal direction.

One inventive aspect of current embodiments is that the processing of the recorded data combines a pixel's edge directionality in two directions—for example, in the x and y directions—and further combine directionality with a magnitude that is proportional to a brightness or intensity of light reflected, or alternatively proportional to a thermal energy emitted. In this regard, the intensity or the thermal energy are associated with the displacement associated with the subject motion which is being studied. When combining in this manner, each pixel's edge or contrast magnitude and direction can be determined. Optionally, an edge threshold value can be applied to the magnitude to reduce the number of pixels that will be used in subsequent processing steps as described in step 330. For example, an overall edge magnitude can be determined by adding each pixel's x and y edge magnitude in quadrature and taking the square root. The direction of the edge can be determined by combining both the x and y direction as described in step 340. Using this information, one can calculate an edge angle for each pixel. For example, the use of functions such as Arc Tangent function is known to determine the angle a vector forms on an x-y plot. The magnitude and phase of the motion at any frequency of interest in the video recording is determined by calculating the FFT of the intensity waveform for each pixel as shown in step 350. Optionally, an amplitude threshold can be applied to these pixel amplitudes to reduce the number of pixels processed in subsequent steps as described in step 360.

The x-y axes are referred to here, but it will be understood that an angle can be determined in relation to any two axes of a recorded scene if the axes are substantially perpendicular to one another. Also, as one considers motion of two edges of a component along a common axis such as the x-axis to be 0 degrees, it is possible to calculate a relative edge direction for each pixel. In such an example, during the study of motion one may designate a contrast change from white to black of a vertical edge as 0 degrees, and a contrast change from black to white at an edge as 180 degrees. Alternatively, one might designate a contrast change from white to black of a vertical edge as 90 degrees, and a contrast change from black to white of a vertical edge may be 270 degrees. In such a scenario the image now has a phase mask that represents the angle of the edges or contrast at each pixel. Using these designations, pixel data associated with an edge angle image can now be used to normalize the phase map that was previously created, by normalizing all pixel modulations that are sensed against the direction or contrast of the edge as described in step 370. The normalization will result in both a white-to-black edge and a black-to-white edge being in phase when the motion of a component associated with those edges actually is in phase. In essence, the practice of such embodiments leads to normalizing all edges such that the transition from varying levels of contrast have the same directionality. Going back to the designations mentioned above, the normalization results in the edge going from black-to-white edge (originally designated as a contrast change of 180 degrees) as though it is 180 degrees. By contrast, without the normalization, the existence of both black-to-white and white-to-black contrast changes at different locations of a component would make the two edges appear to be out of phase.

The phase map can be presented in various forms as an overlay of a single frame or as an overlay or as an overlay on the original video or video which has been modified by amplification, filtering, or other methods as outlined in step 380. These options might include one or more phase masks where all phases from 0 to 360 degrees are mapped to colors in a selected color spectrum. Optionally, the user might identify a specific phase value or the phase at a specific edge of an object in the video recording and the phase mask could identify those pixels moving in-phase or out-of-phase with the identified phase value. Modifications to the phase map options will continue as described in step 390 until the user is ready to examine another frequency of interest as outlined in step 400.

The pixels which are removed by applying a threshold based on edge strength and/or vibration amplitude could either show black or transparent so that the original image is visible. Other options might include specifying a level of transparency for the colored overlay.

Additionally, the phase map could be used to normalize phase vectors drawn on a frame, the original video, or a modified video. The phase vectors may be requested by the user for selected spatial locations, selected regions or objects in the field of view, or for locations on a grid superimposed on the field of view. The normalized phase vectors would be corrected for image contrast effects by adding the edge angle phase vector to the phase of the intensity waveform at each pixel location where a vector is to be displayed.

Now turning back to the component represented in FIG. 23, if the pixels on the horizontal edge denoted by the right dotted rectangle were designated to have an edge directionality of 0 degrees, and the pixels on the horizontal edge denoted by the left dotted rectangle were designated to have an edge directionality of 180 degrees, the phase of modulation would be normalized so that the motion of the component having these horizontal edges would be depicted to have accurate phase when modulation is occurring from the motion of the component itself, rather from contrast with other objects either moving or stationary and having different colors and shades than the machine or component of interest. In this same scenario, the phase of the pixels on the vertical edge denoted by the top dotted circle would have been designated with a phase directionality of 90 degrees, while the pixels on the vertical edge denoted by the bottom dotted circle would have been designated with a phase directionality of 270 degrees. Accordingly, normalization of the vertical motion would result in the same phase being measured for both the top and bottom vertical edge pixels denoted by the dotted circles.

An example of the visual impact of this normalization process applied to a video recording of a checkerboard in motion is given in FIG. 26A-C. Consider a recording of the checkerboard (representing a component in motion) shown in FIG. 26A while being moved vertically with a 10 Hz sinusoidal motion. If the phase is calculated for every pixel in the image, a phase of 0 or 360 degrees is calculated for the edges of all horizontal boundary lines that have a black-to-white transition, identified by label 461 in FIG. 26B. But likewise, a phase of 180 degree is calculated for the horizontal lines between a white-to-black transition, identified by label 462 in FIG. 26B. However, this is not correct because the entire checkerboard seen in the image is being moved as a single entity. When a phase normalization process is applied to the pixels, as shown in FIG. 26C, this effect can be corrected by subtracting 180 degrees (or, in this case, adding 180 degrees) from the result obtained using an operator selected, with respect to one of the transitions which could be either the one from black to white or white to black identified above (461 or 462). Following such a correction, the phase of all horizontal edges will be aligned having value of 0 degrees at these edges where the transitions occur.

In operation, generally a user knows the intended axis of reciprocating motion of a component (again, represented by the checkerboard). If the intended axis is up and down relative to a fixed surface such as a floor, then the interest will be in normalizing the phase of the horizontal edges as done here. By doing so, it facilitates the analysis of the vertical edges that also are part of the checkerboard (representing component) to evaluate anomalous behavior. Accordingly, with knowledge of an expected axis of motion, an analyst (i.e., user) may select which type of edge (in this case, horizontal rather than vertical) to perform phase normalization upon. As shown in FIG. 29, in some embodiments a drop-down menu is provided so this selection can be made. It will be understood that if the motion of the component were parallel to the floor, then phase normalization of the vertical edges represented by the checkerboard would be more beneficial, and an embodiment provides this through such an exemplary drop-down menu.

In the above situation, it is assumed that the component is moving as a single entity at a 0 degree angle relative to an origin. Consequently, only one of the horizontal edge transitions will need to be corrected relative to the motion of this component as a single entity. But in other situations, again using the checkerboard as representative of a component in motion, both transitions (i.e., one edges moving from black to white and one from white to black) could need correction. Suppose, for example, that the entire component is moving at an angle of 45 degrees relative to an origin. In this instance, after an operator is selected and a phase value is calculated for both transitions (461, 462) both phase values may need correction. In this scenario of 45 degree motion of the checkerboard, suppose that transition 461 is calculated to be 0 degrees and transition 462 is calculated to be 180 degrees. One knows that this is not correct because the checkerboard is moving as a single entity, thus the phases at these edges should be the same. Further, neither transition will be 0 degrees or 180 degrees. Since the motion relative to an origin is known to be 45 degrees or can be calculated as such, the correction of phase values will involve adding 45 degrees to the phase value calculated with an operator for edge transition 461, and subtracting 135 degrees (or, in this case, adding 225 degrees) to the phase value calculated with the same operator for edge transition 462.

FIGS. 27A-C provide a second example of applying a phase normalization process, in accordance with present embodiments. This video is a recording of a machine which is vibrating. Motion of the machine is occurring in all 3 directions; such that different mechanical components may demonstrate varying phase relationships to one another. FIG. 26A presents one unmodified frame of the original video. FIG. 27B presents the same frame with an unnormalized phase map superimposed on the video frame, showing the vibrating machine train. In turn, FIG. 27C presents a normalized frame map superimposed on the frame. In FIG. 27B, areas of the top portion of the motor housing identified by area label 471 and area label 472, respectively, appear to be out-of-phase with each other. However, their correct in-phase relationship is seen at area labels 471 and 472, respectively, in FIG. 27C.

A further example of improved phase mapping provided by present embodiments is shown in FIGS. 28A-C. FIG. 28A is a single frame of a bridge experiencing vibration, and an unnormalized phase map is shown in FIG. 28B. Portions of the handrail appearing as a dark line, and identified as 481, are out of phase with the remainder of the handrail. But this is not a correct representation of phase, as the entire handrail is a solid structure, so any vertical slice taken of the handrail clearly is moving in phase together with all other parts of the handrail along this slice. This effect is corrected on the normalized phase map at 481 as shown in FIG. 28C, which shows all vertical slices of the entire handrail as in phase.

While the phase maps presented in the figures represent phase values which are shown in black and white, this is not intended to limit the embodiments provided for herein to a particular type of phase map which is to be applied. In accordance with these embodiments, a phase map could be constructed from a color spectrum (one example of which is a 4-way color wheel as discussed below which uses assigned colors to indicate angles of movement associated with phase at a given point in time) instead of black and white as a preference established by the user; a phase map could be applied to a single frame of a video, or to a modified video which may exaggerate the motion of machines, components, or objects in the field of view; a phase map may present the entire range of 360 degrees, or the user may desire to focus on a smaller range of phase values; and a phase map could be constructed to highlight only phase values in the scene which are in-phase within a range up to a particular range, for example +/−30 degrees, or alternatively to highlight phase values which are out-of-phase with respect to a reference object identified by the user. Also, the present embodiments can be practiced in a manner that allows a user to see normalized phase values for various positions or objects in a field of view, and have those labeled on a single frame, or these features can be practiced producing a phase normalized video, which may or may not be modified by amplifying the motion as desired by a user. All the aforementioned capabilities are illustrative of, but not limiting of, the variations of the phase mapping and normalizing descriptions provided herein.

FIG. 29 is a screen shot 290 from a user interface indicating some aspects of a phase overlay operation including user selections to be performed by the processor and software. In the lower left of the figure is a graphical frequency spectrum 291. When a user inputs a value into frequency selection box 292, the frequency spectrum changes appearance to indicate which frequency is selected as at line 293 in the figure. An orientation drop-down box 294 is shown in FIG. 29, which allows a user to select an orientation of interest, for example “Horizontal Edge” or “Vertical Edge.” In the exemplary image in FIG. 29, a user has indicated to normalize the phase of motion for horizontal edges, meaning that a representation of phase for horizontal edges will be normalized while vertical edges will not be normalized. Other features of the phase overlay screen include a first slider bar 295 and a second slider bar 296 for adjusting a transparency level of the playback and an amplitude threshold, respectively. The amplitude threshold allows user control to have only movements above a selected amplitude of displacement are processed with the phase normalization feature. FIG. 29 also shows a 4-way color wheel 297 to indicate a direction of motion. The 4-way color wheel in FIG. 29 is used to provide a normalized phase map such that +X, −X, +Y, and −Y each is represented by a unique color. In this exemplary color wheel, the representations are red for +X, green for −X, yellow for +Y, and dark blue for −Y.

FIGS. 30A-30B are directed to embodiments providing operations that facilitate a phase study of machinery or components, by accentuating the directionality of edges and phase of motion with reference to a 4-way color wheel. In some embodiments, accentuation is facilitated by emphasizing one or more edges with a color representative of motion for one directional edge, which may be accompanied by an appearance of suppressing a color representative of motion along a different directional edge. In this regard, emphasizing one or more directional edges involves making an appearance of a color assigned to such an edge stand out, such as by increasing the weight or vibrancy of a color line added to the one or more edges during processing, adding gradients, adding dashes or hatching, or other processing known in the art to increase perceptibility in appearance. In general, modifications of a video recording associated with accentuating one or more directional edges in an image frame of the video recording will be proportional to a strength of a given directional edge, after applying a selected operator. Suppressing, on the other hand, involves making or allowing an appearance of a color assigned to an edge to be less perceptible. In some embodiments, this suppressing appearance will manifest as a reduced weight of a color line being added to the edge upon displaying.

FIG. 30A provides an image illustrating an operation directed to accentuating vertical edges of components in moving objects shown in the image, to emphasize horizontal motion. FIG. 30B provides the same underlying image processed differently to illustrate an operation directed to a phase study accentuating horizontal edges of components in the moving objects to emphasize vertical motion. In some embodiments, the processing includes applying an operator to directional edges found on a particular axis of an image frame or image frames. Directional edges along the axis of an image frame to which the operator is applied, e.g., “Horizontal Edge” or “Vertical Edge,” will thus be accentuated, while other edges may appear to be at least partially suppressed. In one of myriad possible use scenarios, if an operator is applied to “Vertical Edge,” the result will be to accentuate at least some vertical edges to emphasize horizontal motion of structures or components in the video; if an operator is selected to apply to “Horizontal Edge,” the result will be to accentuate at least some horizontal edges to emphasize vertical motion of structures or components in the video. The quoted terms in the prior sentence reflect certain phrases contained in the drop-down boxes shown in FIGS. 30A-30B, but different phrases consistent with present embodiments herein could be inserted for selectable options provided in a drop-down box. Accordingly, the result is accentuating at least one directional edge within an image frame or series of image frames, wherein the at least one directional edge that is accentuated emphasizes a directional motion of the moving object shown in one or more image frames of a modified video recording.

Thus, in some embodiments as illustrated in an exemplary manner by FIGS. 30A-30B, to gain more emphasis upon horizontal motion, one or more vertical edges can be accentuated (FIG. 30A), while to gain more emphasis on vertical motion, one or more horizontal edges can be accentuated (FIG. 30B). It will be understood that the options are referred to here as Horizontal Edges and Vertical Edges, respectively, but the machine, component or object being studied does not have to be moving straight up and down or exactly right to left in the image frames for the accentuating effects to be determined. The machine, component or object from which a directional edge is accentuated could be moving at any angle in a video captured by a video recording device, with directional edges that are substantially perpendicular to one another—i.e., positioned at 90 degrees +/−10 degrees. Whether the motion is straight up and down vertical, straight right to left horizontal, or some other angle of motion seen in the video, edges can be selected to be accentuated or suppressed alternately depending on a user selection of “Horizontal Edge” or “Vertical Edge.”

Accordingly, FIGS. 30A and 30B are frames from a video obtained in accordance with present embodiments that provide images of the same arrangement of components and objects at the same point in time. In the context of an exemplary 4-way color wheel in FIGS. 30A-30B (which provides the same color representations as 4-way color wheel 297 in FIG. 29), a phase map is provided such that +X, −X, +Y, and −Y each is represented by a unique color, namely red for +X, green for −X, yellow for +Y, and dark blue for −Y. In some embodiments, colors are blended to present the X-Y motion of each pixel. An addition of color to one or more edges in these figures forms a color phase map indicating phases of motion of various components and particularly edges of objects seen in the images.

As previously mentioned, FIG. 30A accentuates certain directional edges in the image frames of the video recording by performing an operator (e.g., Sobel operator) on the vertical edges. In this regard, FIG. 30A shows a motor 301 and a blower 304 connected with a coupler 302 which is guarded by a coupler guard 303. In FIG. 30A, one observes that vertical edges in an image frame—but not horizontal edges such as shaft 305—are emphasized as seen in a region delineated by a bracket 310, representing a portion of an outer edge of the coupler guard 303. This will be appreciated by examining the relatively bolder shade of yellow color along the edge contained within the region denoted by bracket 310 in the image. Thus, in FIG. 30A, after selecting “Vertical Edge” to accentuate from the drop-down box, this will emphasize horizontal motion in the scene, such as movement of the coupler guard. By comparison, in FIG. 30B one of the vertical edges which had been accentuated in FIG. 30A (but here denoted by a bracket labeled as 310′) appears in a very faint yellow color through suppression of vertical edges occurring in FIG. 30B. Accordingly, the boldness or faintness of the shade can reflect the phase of motion at a given point in time and the strength of the signal produced in view of the operator being applied by the processor. For example, selecting “Vertical Edge” in the drop-down box causes an operator to be applied by the processor to accentuate at least one vertical edge component (in FIG. 30A, denoted by region 310) of the machine or object shown, to emphasize the horizontal motion of the machine or object (i.e., coupler guard 303).

By comparison, FIG. 30B emphasizes vertical motion in a video, by performing an operator (in a non-limiting case, a Sobel operator) to accentuate horizontal edges seen in the image frame. In a hypothetical instance of vertical misalignment, a first end of shaft 305, located closer to motor 301, may move slightly upward while a second and opposite end, located closer to coupler guard 303, may move slightly downward simultaneously. These movements may be so slight as to make them imperceptible to the human eye when viewing a video recording of the shaft and neighboring machinery. Thus, in regards to the shaft as seen in both figures, but in FIG. 30B labeled as 305′, the operation illustrated in FIG. 30B accentuates certain horizontal edges of the shaft. That is, as shown in FIG. 30B, a user may select Horizontal Edge from the drop-down box to accentuate the directional edge of the shaft labeled as 305′, which is to emphasize vertical motion. Accordingly, in FIG. 30B, phase information is provided for shaft 305′ (at least). In FIG. 30B, a user selecting “Horizontal Edge” in the drop-down box causes an operator to be applied by the processor to accentuate at least one horizontal edge component of the machine or object shown, to emphasize vertical motion of the machine or object.

The effects of this processing on vertical edges would be relatively suppressed in FIG. 30B (as seen by a comparison with the region denoted by bracket 310 in FIG. 30A). Returning to the hypothetical presented above, if the shaft represented as 305 and 305′ in the two figures were in fact mis-aligned, one characteristic appearance seen in a processed image such as shown in FIG. 30B might be one color at the first end of shaft 305′ closer to the motor, which differs from the color at the opposite end closer to the coupler guard. For example, and again hypothetically, if the end of shaft 305′ closer to the motor was represented as yellow it would signify 0 degrees of motion (basically upward), but if the opposite end closer to the coupler guard was represented as dark blue, it would signify an opposite phase of 180 degrees motion (basically downward). There could be instances where this is expected, but in other use cases an analyst may be prompted by such a presentation to check for misalignment. While there are myriad possible ways these principles could be used advantageously, taken together FIGS. 30A-30B provide an exemplary use case for selectively using operators, e.g., Sobel operators, to accentuate an appearance of certain directional edges and suppress others to emphasize particular motions in a scene of a video recording. Following processing such as described for FIGS. 30A-30B, a system configured according to present embodiments displays a modified image frame in a video recording containing the moving object, and superimposes in the video recording at least one modified phase value associated with the moving object. Such modified phase value again may be, but is not limited to, a color phase map assigning a color for a given phase of motion. In some embodiments, accentuating a horizontal edge component as in FIG. 30B as the at least one directional edge accentuated will emphasize vertical motion of the moving object in the image frames. In turn, accentuating a horizontal edge component as in FIG. 30A as the at least one directional edge accentuated will emphasize horizontal motion of the moving object in the image frames.

It will be understood that the embodiments described herein are not limited in their application to the details of the teachings and descriptions set forth, or as illustrated in the accompanying figures. Rather, it will be understood that the present embodiments and alternatives, as described and claimed herein, are capable of being practiced or carried out in various ways. Also, it is to be understood that words and phrases used herein are for the purpose of description and should not be regarded as limiting. The use herein of such words and phrases as “including,” “such as,” “comprising,” “e.g.,” “containing,” or “having” and variations of those words is meant to encompass the items listed thereafter, and equivalents of those, as well as additional items. Similarly, use of phrases containing the word “inventive” are not intended to limit the scope of embodiments to a single method, technique, or aspect, but rather contemplate the existence of multiple embodiments as described herein as well as variations and alternatives based on the current teachings and descriptions.

Accordingly, the foregoing descriptions of several embodiments and alternatives are meant to illustrate, rather than to serve as limits on the scope of what has been disclosed herein. The descriptions herein are not intended to be exhaustive, nor are they meant to limit the understanding of the embodiments to the precise forms disclosed. It will be understood by those having ordinary skill in the art that that modifications and variations of these embodiments are reasonably possible in light of the above teachings and descriptions. 

What is claimed is:
 1. A system for evaluating a moving object undergoing periodic motion, employing at least one video acquisition device, the system comprising: a processor configured to cause the video acquisition device to acquire sampling data, wherein the sampling data comprises a video recording of a scene having a plurality of image frames of the moving object, wherein an image frame is divisible into a plurality of pixels; and a computer program operating in the processor to: operate on one or more pixel intensities at a selected frequency of motion present in the video recording; automatically calculate a phase value for one or more pixels at the selected frequency and implement a normalizing phase adjustment, wherein the normalizing phase adjustment is dependent on a direction of displacement of the moving object; and display phase values that reflect the normalized applied.
 2. The system of claim 1, wherein a normalized phase map is applied to one or more frames of the video as it is replayed based on at least one of the plurality of image frames as selected by the user.
 3. The system of claim 1, wherein one or more normalized phase vectors is shown on at least one of the plurality of image frames for a position in the field of view identified by the user.
 4. The system of claim 1, wherein one or more normalized phase values is labeled on at least one of the plurality of image frames for a position in the field of view identified by the user
 5. The system of claim 1, wherein the normalizing phase adjustment also is dependent on a direction of contrast between the moving object and at least one other structure in the scene.
 6. The system of claim 1, wherein the normalizing phase adjustment to determine edges is performed by canny edge detection or a gradient operator.
 7. The system of claim 6, wherein the normalizing phase adjustment to determine edges is performed by a gradient operator which is a Sobel operator.
 8. The system of claim 2, wherein the moving object comprises a plurality of components, and the one or more normalized phase values is calculated for a subset of the pixels in the field of view as defined by the user using a graphical user interface.
 9. The system of claim 2, wherein the computer program further operates to provide a modified version of the video recording as it is replayed, wherein the modified version produces motion amplification of the displacement of the moving object, wherein the motion amplification is performed digitally to visually exaggerate apparent movement of the at least one object.
 10. A method of visualizing and studying motion of a moving object, comprising: acquiring a video recording of a scene that contains the moving object, wherein the video recording comprises a plurality of image frames of the moving object and wherein an image frame is divisible into a plurality of pixels; selecting a frequency of motion present in the video recording; automatically performing a normalizing phase adjustment upon measured phase values of pixel intensity, wherein the normalizing phase adjustment is dependent on a direction of displacement of the moving object; displaying a modified image frame containing the moving object; and superimposing in the recorded video at least one modified phase value associated with the filtered motion.
 11. The method of claim 10, wherein the normalizing phase adjustment is dependent on a direction of contrast between the moving object and at least one other structure in the scene.
 12. The method of claim 10, wherein a normalized phase map is applied to one or more frames of the recorded video as it is replayed based on at least one of the plurality of image frames as selected by the user.
 13. The method of claim 10, wherein one or more normalized phase vectors is shown on at least one of the plurality of image frames for a position in the field of view identified by the user.
 14. The method of claim 10, wherein one or more normalized phase values is labeled on at least one of the plurality of image frames for a position in the field of view identified by the user.
 15. The method of claim 10, wherein the normalizing phase adjustment to determine edges is performed by canny edge detection or a gradient operator
 16. The method of claim 15, wherein the normalizing phase adjustment to determine edges is performed by a gradient operator which is a Sobel operator.
 17. A method of visualizing and studying motion of a moving object, comprising: acquiring a video recording of a scene that contains the moving object, wherein the video recording comprises a plurality of image frames of the moving object and wherein an image frame is divisible into a plurality of pixels; accentuating at least one directional edge within an image frame by applying an operator to the image frame, wherein the at least one directional edge emphasizes a directional motion in the image frame; displaying a modified image frame in the video recording containing the moving object; and superimposing in the video recording at least one modified phase value associated with the moving object.
 18. The method of claim 17, wherein accentuating a horizontal edge component as the at least one directional edge will emphasize vertical motion of the moving object.
 19. The method of claim 17, wherein accentuating a vertical edge component of the moving object emphasizes horizontal motion of the moving object.
 20. The method of claim 17, wherein the operator applied is canny edge detection or a gradient operator
 21. The method of claim 20, wherein the operator applied is a gradient operator which is a Sobel operator.
 22. The method of claim 17, wherein a phase map is automatically applied to one or more frames of the video recording as it is replayed based providing a representation of color to emphasize the at least one motion associated with the moving object. 